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Australian Government Research Training Program Stipend Scholarship

Australian Government merit-based scholarship for new research degree candidates.

Griffith University Postgraduate Research Scholarship

Griffith University merit-based scholarship for new research degree candidates.

Australian Government Research Training Program International Fee Offset Scholarship

Australian Government merit-based tuition scholarship for new international research degree candidates.

Griffith University International Postgraduate Research Scholarship

Griffith University merit-based tuition scholarship for new international research degree candidates

Griffith University Indigenous Australian Postgraduate Research Scholarship

Available to new Aboriginal and Torres Strait Islander research degree candidates.

Griffith University Disability Scholars Research Scholarship

Griffith University scholarship for domestic and international research degree candidates who have a disability.

Griffith Industry PhD Scholarship Scheme

Undertake research in collaboration with an industry partner, providing practical experience while you complete your doctoral studies.

Australian University Honours Entry Postgraduate Scholarship

Griffith University merit-based scholarship for domestic research degree candidates who have completed (or will complete) a bachelor degree with honours or an pathway program.

Bacterial subversion of metal ion intoxication

Griffith merit-based scholarship investigating the molecular mechanisms used by pathogenic streptococci to avoid killing by metal stress

Linking creative practice with cultural heritage preservation

Griffith merit-based scholarship investigating how cultural heritage institutions can better support culturally diverse communities through preservation of their cultural heritage

Ecosystem characterisation for marine spatial planning using hydroacoustics

Griffith merit-based scholarship exploring incorporation of hydroacoustic (echosounder) datasets into marine spatial planning using data analysis, mathematical modelling, and ecological application of analytical results.

Shifting livelihoods in a post-logging economy

Griffith merit based scholarship exploring the impact of Solomon Island village livelihoods, how people are moving forward and how opportunities for sustainable and equitable futures can be maximised

Working towards equitable prevention strategies for breast cancer-related lymphoedema

Griffith merit based scholarship aiming to shift the paradigm from arresting progression to the prevention of breast cancer-related lymphoedema

Hyperspectral Imagery and Deep Learning for Greenhouse Gas Monitoring

Griffith merit based scholarship focused on developing advanced methods for greenhouse gas (GHG) monitoring using hyperspectral satellite imagery and deep learning.

The key to the long-term health of Australian rivers: nutrient assimilative capacity

Griffith merit based scholarship exploring nutrient biogeochemistry in river systems and the challenging issue of assessing the ability of rivers to process nutrients from multiple sources within catchments.

MAIC Griffith University Road Safety Research Collaboration

Grant funded scholarship examining the fields of traffic psychology and work driving behaviours

The Hopkins Centre - Research for Resilience and Rehabilitation

The Hopkins Centre: Research for Rehabilitation and Resilience supports applied research in an interdisciplinary research environment that brings clinicians, citizens, researchers, academics, and industry together to conduct research of relevance to brain injury, spinal cord injury, persistent pain and other chronic disabling conditions

CSIRO Industry PhD Program: Detecting and assessing hail impacts on solar energy prosumers

Industry linked scholarship project that examines the influence of extreme weather events, especially hailstorms, on the performance of solar panels and their electricity output.

Hyperspectral Imagery and Deep Learning for Greenhouse Gas Monitoring

Industry linked scholarship project focused on developing advanced methods for greenhouse gas (GHG) monitoring using hyperspectral satellite imagery and deep learning.

The next generation of OEC therapy for spinal cord injury repair

Griffith merit based scholarship project focusing on the development of cell-based therapeutic constructs for spinal cord injury repair

Investigating structural plasticity in virus capsids

Griffith merit based scholarship investigating the ability of both native and engineered viral proteins to assemble into capsids with varying geometry

Universal Jurisdiction for International Crimes in Asia-Pacific

Griffith merit based scholarship analysing and comparing the approaches of the African Union and the Association of Southeast Asian Nations, as intergovernmental organisations, toward prosecuting international crimes (crimes against humanity, war crimes, genocide, and aggression) on the basis of universal jurisdiction.

ACSPRI Fellowship Program

Australian Consortium for Social and Political Research Inc Fellowship program valued at $25,000

Allison Milner Memorial PhD Scholarship

The Allison Milner Memorial PhD Scholarship was established by MATES in Construction to honour the legacy of Ass Prof Allison Milner. The total Scholarship value is up to $108,000 over three to five years. PhD Scholarships are open to Australian graduate researchers undertaking research addressing suicide prevention initiatives for blue collar, male dominated industries in Australia.

ANSTO (Australia's Nuclear Science and Technology Organisation)

These scholarships support early career researchers working on industry-focused projects in health, radiopharmaceuticals, defence, space, environment, nuclear fuel cycle and nuclear technologies. Successful candidates work under the guidance of some of Australia’s leading scientists. They may also obtain access to ANSTO’s state-of-the-art facilities to put their research to the test and develop workable solutions to real world problems.

ANZSBT (The Australian and New Zealand Society of Blood Transfusion)

PhD Scholarship and Research Grants offered by the Australian and New Zealand Society of Blood Transfusion (ANZSBT). Please click on the specific tab for the applicable funding opportunity

Australia Awards

Griffith University is proud to partner with the Australian Government to equip award recipients with further skills, knowledge and education to drive change, and contribute to the development and growth of their home country.

Australian Fulbright Postgraduate Opportunities

Fulbright Postgraduate Scholarships support graduate students undertaking their Australian PhD to conduct research in the U.S. as a Visiting Student Researcher (VSR)

Australian Rotary Health

Rotary Health will provide stipend and incidental support for a postgraduate student who is undertaking full time research in specific areas of Health research

Blue Economy CRC

The Blue Economy CRC places heavy emphasis on Education & Training, with an unprecedented scale of research opportunities on offer, including fully funded Higher Degree by Research (HDR) PhD scholarships across its five research programs.

CSIRO Postgraduate Scholarships

Scholarships provide the future generation of scientists with the opportunity to launch their scientific careers working alongside leading scientists in world class facilities to deliver real world solutions.

DAAD

The German Academic Exchange Service (DAAD) is an organisation that promotes the international exchange of students and scientists.This programme offers you the opportunity to do a bi-national doctoral degree with bi-national supervision at your home university and at a university in Germany. Grants are initially awarded for up to 12 months.

Evolving Digital

The Digital Innovation Scholarship is designed to pioneer the next generation of digital-first entrepreneurs. Including on-the-job digital marketing experience, mentorship from our digital marketing experts, and a $5,000 scholarship to help you evolve your digital marketing skills.

Grain Research and Development Corporation

Research scholarships to attract and foster the talent required to conduct and drive world-class grains RD&E.

JDRF Australia: PhD Top-up Scholarship and Travel Grants

JDRF Australia offers the PhD Top-up Scholarship to support and encourage PhD students pursuing research in the field of type 1 diabetes and the travel grant to support students to participate in national or international scientific conferences and forums held either virtually or in-person.

QUAD Fellowship

This program will sponsor 100 exceptional American, Japanese, Australian, and Indian master and doctoral students in science, technology, engineering, and mathematics (STEM) to study in the United States

University Grants Commission of Sri Lanka (UGC) and Griffith University PhD Scholarship

University Grants Commission of Sri Lanka (UGC) and Griffith University have established a joint funding program to allow up to twelve suitably, UGC supported candidates to enrol in the Doctor of Philosophy program at Griffith University each year.

Crawford Fund

The Crawford in QLD Student Awards provide support for students to incorporate a novel international agricultural research experience into their current program.

Suicide Prevention Australia

The PhD Scholarship provides funding for successful applicants to attain a Doctor of Philosophy (PhD). The PhD Scholarship is a funding agreement between Suicide Prevention Australia and the applicant Administering Institution (a University in Australia).

Wine Australia‚ PhD and Masters by Research scholarships

Wine Australia offers a PhD and Masters by Research scholarship program to help attract postgraduate students to the fields of wine, viticulture and wine business research.

Indigenous Politics

The scholarship opportunity is open to Aboriginal & Torres Strait Islander Australians and has a total value of $52,000 per annum

The Prof Anna Stewart Scholarship Program supporting First Nation PhD Students

Top up scholarships supporting First Nations candidates to undertake a PhD, awarded in honour of Prof Anna Stewart

South Pacific Central Banks and Griffith University PhD Scholarship

South Pacific Central Banks and Griffith University have established a joint funding program to allow up to four suitably qualified, Central Bank supported candidates to enrol in the Doctor of Philosophy program at Griffith University each year.

Higher Education Commission (HEC) Pakistan & Griffith University PhD Scholarship

The Higher Education Commission, Pakistan & Griffith University have established a joint funding program for selected graduates from Pakistan to complete a PhD at Griffith.

China Scholarship Council (CSC) and Griffith University

Joint-funded scholarships for new CSC research degree candidates covering tuition fees, Overseas Students Health Cover, plus more...

Griffith University and University of Chinese Academy of Sciences Joint Doctoral Program

Griffith University and the University of Chinese Academy of Sciences merit-based scholarship providing a living allowance and covering tuition fess and overseas student health cover. The program is for Chinese applicants from all research areas including natural resources, environment and life sciences, and any other areas where there are existing or proposed research collaborations.

Department of Science and Technology (DOST) with Science Education Institute (SEI) and Griffith University PhD Scholarships

Griffith University and the Department of Science and Technology (DOST) with Science Education Institute (SEI) merit-based scholarship providing a living allowance and covering tuition fess and overseas student health cover. The favoured areas of research for this program are under the themes of soil ecology and water management, ecology, and other related fields, and in connection with existing projects supported by DOST-PCAARD and DOST-PCIEERD. Other areas can be considered on a case-by-case basis.

Manipal Academy of Higher Education (MAHE) and Griffith University Collaborative PhD

Griffith University and the Manipal Academy of Higher Education (MAHE) and Griffith University have established a jointly supervised PhD program to support suitably qualified applicants nominated by MAHE to enrol in the Doctor of Philosophy program at Griffith University. The favoured areas of research for this program include pharmacy, health and biological sciences, engineering, and natural sciences. Other areas can be pursued and considered on a case-by-case basis.

Agusan del Sur State College of Agriculture and Technology (ASSCAT) and Griffith University PhD Scholarships

Agusan del Sur State College of Agriculture and Technology (ASSCAT) and Griffith University have established a joint funding program to support suitably qualified ASSCAT staff members to enrol in the Doctor of Philosophy program at Griffith University. The favoured areas of research for this program are under the themes of environmental science, agriculture, engineering, education, arts and sciences, information science and technology, business and entrepreneurship and other related fields. Other areas can be considered on a case-by-case basis.

China Scholarship Council (CSC) Sino-Australian Innovative Talents in Tourism Program – Griffith Business School and Sun Yat-Sen University (SYSU)

Sun Yat-Sen University (SYSU) and Griffith University have established a jointly supervised PhD program to support SYSU graduates of exceptional research potential to undertake a Doctor of Philosophy program, supported by the China Scholarship Council (CSC) Sino-Australian Innovative Talent in Tourism Program.

Ristekdikti and Griffith University

The Indonesian Ministry of Research, Technology and Higher Education (Ristekdikti) has reestablished the Overseas Postgraduate Scholarship (Beasiswa Pendidikan Pascasarjana Luar Negeri, or BPPLN) program. Applicants must be a permanent lecturer in a university under the remit of Ristekdikti to be eligible for a PhD scholarship.

3D Data Visualisation and the Metaverse

Virtual environments in the Metaverse provide almost infinite visual real estate for interacting with 3D data visualisations. This provides opportunities for novel deployment of large data sets across diverse domains, including health, climate, education, defence, cybersecurity, blockchain, etc. However, there are significant challenges to useful data engagement in the Metaverse where information and users may be distributed across environments but still need to collaborate. This project will explore the visualisation of 3D data using immersive head-mounted displays that support eXtended Reality (XR) and develop new paradigms for virtual and real-world data interaction into, within and out of the Metaverse.

A new therapeutic model for Parkinson's disease: physiological and pathological roles of potassium channels

Parkinson's disease (PD) is an ageing-related, multifactorial neurological disorder featuring selective degeneration of dopaminergic neurons in the midbrain. The mechanism underlying the loss of dopaminergic neurons is complex and still elusive. However, ion channels have been shown to play an important role in neurodegeneration due to their fundamental functions in neuronal excitability. In this project, we aim to expand our recent findings on the potential pathological role of potassium channels in Parkinson's disease, and elucidate the underlying molecular mechanisms using a dopaminergic neuron cell model and knockout mouse model.

Accurate estimation of sugarcane yield from aerial remote sensing data

Crop yield estimation plays a significant role in management of agricultural activities and decision making, such as fertiliser (e.g., nitrogen) use, crop insurance, harvesting and storage requirements, and budgeting. Visual yield assessments by growers or agronomists could be highly subjective and labour intensive. Methods based on satellite RGB image along with existing agronomic and meteorological data compute in-season vegetation indices often estimate yield for an entire district or region, so they lack in farm-specific yield estimation. Moreover, sugarcane yield estimation does not depend much on the 2D spectral information (i.e., leaf-colour), rather on the plant height and stalk density. Thus, the project will investigate the use of 3D point (aerial laser scanning) data for accurate sugarcane yield estimation. These data will provide important cues to estimate number and density of sugarcane stalks.

Adaptation of the whale watch industry to climate change

Globally, the whale watching industry has been increasing in size and economic value since the 1990s. Whale-watching tourism has transformed entire local communities and contributed significantly to economies. The whale-watching industry, and the whales themselves, face uncertain threats from multiple pressures. This includes the impacts of increased sea surface temperature, altered currents and changes in food abundance on whale behaviour. The research project will look at adaptations of the whale watch industry to changing whale distributions and abundance, drawing from two primary species for Australian waters for which the Whales & Climate Program has data on climate change impacts. This study involves modelling, social and economic science, with a focus on sustainable tourism.

Adaptive Privacy Protection for Medical Data

With the evolution of modern information technology, medical data sharing has become a part of our daily life, which greatly benefits medical research and development professionals, service providers and the society as a whole. The rapidly growing volume and variety of medical data in the past decade has made privacy protection an increasing concern to data owners, obstructing medical data sharing to reach its expected scientific and market value. This project will focus on a solution to provide an adaptive protection for medical data so that data users can create the explainable intelligence based on the shared data without disclosing any privacy information.

Advance native MS methods to membrane proteins enable screening of small molecules against this important class of targets

Major membrane protein (MP) functional groups such as the GPCRs and ion channels, alone make up approximately 56% of protein drug targets. This project aims to develop a universally applicable MS-based label free method that allows rapid screen for MPs. This project will revolutionise and dramatically accelerate the drug discovery process by developing new native MS-based tools that can find novel therapeutic ligands that target MPs.

Advancing green electrochemical engineering of functional 2D nanomaterials

This project aims to produce value-added functional 2D nanomaterials by advancing the green, scalable and costeffective electrochemical production method developed by the candidate. In addition to developing transformational electrochemical engineering technology to utilise Australian raw resources, this project will generate new knowledge in the area of materials chemistry and innovative additive manufacturing technology. Expected outcomes of this project include improved pilot-scale electrochemical reactors for producing various functional 2D nanomaterials and enabling precise control of their molecular and bulk properties. These tailored 2D nanomaterials will significantly improve the performances of flexible and energy-related devices.

Adversarial Attacks on Machine Learning-Based Models in Cybersecurity of Cyber-physical systems

Modern Intrusion Detection Systems (IDSs) rely on machine learning for detecting and defending cyber-attacks in information technology (IT) networks. However, the introduction of such systems has introduced an additional attack dimension; the trained IDS models may also be subject to attacks. The act of deploying attacks towards machine learning-based systems is known as Adversarial Machine Learning (AML). The aim is to exploit the weaknesses of the pre-trained model which has “blind spots” between data points it has seen during training. More specifically, by automatically introducing slight perturbations to the unseen data points the model may cross a decision boundary and classify the data as a different class. As a result, the model’s effectiveness can be reduced as it is presented with unseen data points that it cannot associate target values to, subsequently increasing the number of misclassifications. Adversarial machine learning attacks and automated detection of these attacks in computer networks will be investigated in this project. This project aims to investigate adversarial attacks to machine learning in cybersecurity of cyber-physical systems and propose mitigation techniques to defend against these attacks.

Adversarial Machine Learning in Cybersecurity Domain

Modern Intrusion Detection Systems (IDSs) play a vital role in safeguarding information technology (IT) networks against cyber-attacks. IDSs rely on machine learning techniques to analyse network traffic and identify suspicious patterns and anomalies that may indicate an ongoing or impending attack. However, the deployment of these machine learning-based systems has introduced an additional vulnerability, as the models they use to detect and respond to threats may also be subject to attacks. This type of attack is known as Adversarial Machine Learning (AML) and involves exploiting the underlying machine learning algorithms to evade detection, misclassify data, or manipulate the training process. This project will study AML techniques in cybersecurity domain and propose defence strategies.

Air route development - feasibility calculation

Air route development (ARD) is a well-known business process within airports and airlines. Airports are usually perceived as leading partners in this stakeholder engagement, aiming to attract new and retain existing airline partners. Still, multiple other stakeholders are involved in the process, and their support or lack of it significantly impacts the sustainability of a particular route. This project aims to propose new ARD feasibility calculation methods that airports, airlines, and other involved stakeholders could use as decision-making tools. Commercial air routes are the primary focus of this project, with the potential to extend it to cargo routes.

Ameliorating microglia-mediated neuroinflammation for Parkinson's disease therapy

Microglial cells, the CNS-resident macrophages, are privileged to be the immune-competent cells of the central nervous system. A large body of evidence supports that microglial cells play a crucial role in mediating neuroinflammation as a significant contributing factor in the progression of ageing-related neurological disorders, including Parkinson's disease (PD). As an essential trigger of abnormal microglial activation, oxidative stress is also a key pathological factor in PD. This project aims to explore how oxidative stress-sensitive ion channels contribute to microglia-mediated neuroinflammation in PD and whether targeting these ion channels may represent a neuroprotective approach to mitigate PD progression.

Ancient DNA

This project aims to recover all the genetic information from four ancient humans. Two of these iconic specimens come from Australia and two from Malaysia. We will sequence the entire DNA (genomes) and proteins (proteome) of Mungo Man (Willandra), as well as the Yidinji King (Cairns), the Deep Skull (Borneo) and the Bewah specimen (Malaysian Peninsula). This will provide a better understanding of the settlement of Australia and new knowledge about the ancient people of Australasia and their relationship to other human populations worldwide. The research will use cutting-edge methods of DNA and protein sequencing of ancient human material and will provide critical reference genomes / proteomes that will anchor future research.

Antibiotic-nanoparticle conjugate systems and their effectiveness assessments against gram negative bacteria

Antimicrobial therapies have been a magic bullet against infectious diseases since their introduction. However, due to the excessive use of antibiotics via irrelevant and unregulated access, the efficacy of the antibiotic has declined rapidly in parallel with increases in antibiotic resistant bacterial strains. Resistance to this antibiotic has risen rapidly and its clinical usefulness has declined to a point that it is now rarely considered a frontline treatment option. With the emergence of resistant Gram-negative ‘superbugs’, infections caused by multidrug-resistant Gram-negative bacteria have been named as one of the most urgent global health issues due to the lack of effective drugs. Numerous research for new antibiotics focus on developing improved versions of existing molecules, Amongst these new designed and engineered drugs, nanosized particles have gained much recent attention due to their physical size, biocompatibility and functionalities. Nanoparticles are expected to provide a localized cure for complex diseases by facilitating targeted delivery and improved bioavailability. The functionalized nanoparticles can either act as the vehicle for potent drugs or they themselves can act as the therapeutic agents. We have developed antibiotic conjugated carbon-based nanoparticle systems and the conjugated systems displayed notable antibiotic effects on various gram-negative bacteria, including those resistant to the antibiotic moiety conjugated onto the nanoparticle. The aim of this project is to extend these systems to include different antibiotic moieties to construct a range of effective antibiotic conjugate analogues onto the nanoparticles.

Antimicrobial probiotics and mechanism study

The inclusion of probiotics in animal feeds have proven to be beneficial to animal health. This project is a collaborative research program between Griffith University and Bioproton, aiming at investigating the mechanism of action of probiotics that have antimicrobial activity. The outcome of the project will lead to scientific discovery on the antimicrobial behaviours of probiotics. The active Bacillus strains can be used as effective antimicrobial agent in animal feed materials.

Application of biocatalytic protein cages in drug discovery and metabolism.

We have developed a number of virus-derived protein cages into robust containers for enzymes. In addition, we are constructing hybrid biomaterials with properties tailored to working with different classes of small molecules. There are a number of project opportunities on the application of biocatalytic protein cages in drug discovery and metabolism.

Artificial Intelligence facilitates ICU Resource Management against Infectious Diseases

Respiratory infections and antibiotic resistant bacterial infection are some of the conditions that have significantly stressed our hospital ICU. A number of risk factors have been reported to associate with severe diseases, which includes age, pre-existing conditions, pathogen setpoints, responsiveness to therapeutic strategy. Any single risk factor is unlikely to be an absolute determinate of clinical diseases, rather many contributors or associations are important with the disease progress. We will use ICU data and artificial intelligence to generate a prediction algorithm to assist clinical decision making.

Artificial Intelligence Support for Adaptive User Interfaces

In both real and simulated environments, people can be overwhelmed if exposed to high levels of competing stimulus which can negatively impact cognitive load. When using immersive technologies, for example for augmented reality, there are opportunities to add useful virtual objects into a real-world environment both as objects located in 3D space and as part of an extended user interface (UI), i.e., a head-up display (HUD). These elements need to be managed as not to diminish user experiences.

Assessing the impacts of chemical pollutants on marine wildlife using novel and ethical techniques

Help us save the sea turtles! Chemical contaminants are accumulating in marine wildlife worldwide. However, due to their large size and often protected status, there are ethical and logistical constraints in conducting traditional whole animal toxicity tests on these animals. Recently, cell-based bioassays have been proposed as an ethical alternative to assessing the effects of contaminants in marine megafauna. This project aims to establish marine wildlife cell cultures and develop species-specific cell-based toxicity bioassays to assess the effects of chemical pollutants in marine wildlife. This project will involve both field and lab components, and include collaborations with state and federal government agencies, non-profit conservation organisations and the private sector

Atomically Thin 3d Transition Metal Electrocatalysts for Water Splitting

The current industrial-scale hydrogen productions are reliant on high temperature steam reforming fossil fuels, consuming large quantity of energy and fossil resources, and emitting huge amounts of CO2. This project aims to develop cheap and plentiful transition metal-based high performance water splitting electrocatalysts, enabling economically viable large-scale water electrolytic hydrogen production driven by renewable electricity. A theory-guided catalyst approach will be used to guide the efficient design and development of high performance electrocatalysts. The success of the project will lead to a suit of high performance water splitting electrocatalysts, leaping forward water electrolytic hydrogen production technology.

Augmented stream learning of medical time series data for continuous outcome prognosis

This project aims to develop novel stream learning algorithms for continuous patient outcome prognosis by taking into account patient's data collected during ICU admission in a unified manner. The algorithms are expected to integrate high frequency time series data with patient's demographic data, lab data, diagnosis data, prescription data, etc. as exemplified in MIMIC-III, for accurate outcome prognosis. Issues such as prediction bias, data leakage, data sparsity, non-stationarity, model explainability will be investigated.

Automated cyber threat hunting using NLP

Advanced cyberattacks pursue their victims over months or years until they can reach their final goal. Detecting these threats in early phases before the final stage of attacks can be executed against endpoint devices can help prevent adversaries from achieving their goals. Many organisations use cyber threat hunting to proactively detect hidden intrusions before they cause a major breach. The goal of hunting is detecting threat actors early in the cyber kill chain by searching for signs of an intrusion and then, providing hunting strategies for future use. An emerging method in cyber threat hunting is using Natural Language Processing (NLP) methods to automate the hunting process. This project aims to investigate and develop practical threat hunting approaches using NLP methods. This method can be used to automate the extraction of indicators of compromise (IoCs).

Automated monitoring of restored marine environments

There is currently a surge in interest in marine and coastal restoration, with a significant number of projects underway, and many more planned. Current methods for monitoring restoration progress and success vary enormously, with low uptake of technological advances that promote efficiency and comprehensiveness. This project will work towards a coordinated, open-science approach to monitoring, that standardises data formats, allows trade-offs or synergies between ecological, socio-economic and cultural benefits to be explored, and facilitates cross-project comparisons and benchmarking. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams.

Automated monitoring of restored marine environments

There is currently a surge in interest in marine and coastal restoration, with a significant number of projects underway, and many more planned. Current methods for monitoring restoration progress and success vary enormously, with low uptake of technological advances that promote efficiency and comprehensiveness. This project will work towards a coordinated, open-science approach to monitoring, that standardises data formats, allows trade-offs or synergies between ecological, socio-economic and cultural benefits to be explored, and facilitates cross-project comparisons and benchmarking. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams.

Automation of Cyber Threat Intelligence phases using Machine Learning

Cyber threat intelligence (CTI) refers to knowledge about potential threats, which includes information on threat indicators such as Tactics, Techniques, and Procedures (TTPs), IPs, and more. CTI can help organisations identify existing threats, either through external open-source threat intelligence or by monitoring adversarial activities within their own networks. The generated CTI can be used to build intelligence about threats against a specific target. Initially, indicators of compromise (IoCs) are generated, and these IoCs can be processed and shared using CTI sharing techniques. Such techniques allow security analysts to use CTI information from other companies and share their IoCs with trusted partners, which can be used to update detection rules and blacklists in security devices such as IDSs and firewalls. To enable effective and collaborative cyber threat intelligence sharing, the application of state-of-the-art machine learning techniques in the CTI generation and sharing should be investigated. This project will review automation of CTI generation and sharing using machine learning . The efficacy of using machine learning technology for detecting network attacks has been widely studied, but it has been difficult to create an ML-based detection system that can handle diverse network data samples from different organisations. This project aims to propose an automated cyber threat intelligence sharing using machine learning that enables multiple organisations to work together to share their IoCs.

Better prediction of chemistry on computers (applied mathematics/physics project)

The future of scientific advancement will certainly involve a mixture of computational prediction and experiment. This interdisciplinary research theme promises to develop next generation theories for better modelling of chemistry, using novel mathematical and physical models. Suits students with a strong interest in applied mathematics and computers. Machine learning techniques will likely feature in this project.

Biosolid-based biochars for agricultural carbon sequestration

Biochar is a solid by-product of thermochemical conversion of biomass (in the absence or reduction of oxygen) to bio-oil and syngas, which is dominantly composed of aromatic compounds resistant to biological degradation. Biochar would enhance soil aeration, increase soil pH, favour nitrogen immobilization, interact with available organic C and N in soil, act as an electron shuttle for soil microorganisms and modify soil enzyme activities as well as microbial abundance and community composition. This project aims to investigate how modification of pyrolysis process (i.e., pyrolysis temperature; heating rate; residence time) and co-pyrolysis of biosolid with organic wastes (i.e., feedstock type; blending ratio) would reduce the environmental risks associated with biosolid (i.e., heavy metals; microplastics; PAHs; PFAS), while improve its quality (i.e., C content, specific surface area; porous structure; water holding capacity) for application in agricultural systems.

Biotechnology with persistent plant viruses

We have determined the first structure of a persistent plant virus. It is not clear what advantage these asymptomatic viruses confer in order to maintain the purported symbiotic relationship they have with their hosts. Understanding the form and function of persistent viruses through molecular and structural biology will open many possibilities for their use in plant biotechnology.

Blue-green algae and their toxins - can we manage them in a changing climate?

Blue-green algal blooms dominate many Australian lakes and reservoirs. Toxic species create major problems for drinking water and recreation. We work collaboratively with environmental and water managers to determine the factors controlling these blooms with both field and lab work.

C. elegans as a model system to characterise the role of a metabolic master regulator

Previously we showed the enzyme dihydrolipoamide dehydrogenase (DLD) to be a metabolic master regulator. We now will characterise the role of DLD in the metabolic network of C. elegans by using metabolomics and biophysical techniques in isolated mitochondria, as well as curating the genome scale metabolic model of C. elegans in collaboration with the WormJam consortium and simulating the nematode’s metabolism.

Can restoration recover lost ecosystem services?

As restoration projects gain traction during this Decade for Ecosystem Restoration, we need to develop techniques that secure the success of these projects and achieve expected outcomes. The project works with many stakeholders including Traditional Owners, farmers, State Government and local council to determine whether ecosystem services, including nutrient retention, carbon sequestration and biodiversity, develop within current wetland restoration projects.

Can we use revegetation to control algal blooms? The role of organic matter leached from trees

Our research has found that leaves from trees leach organic matter that can negatively effect algae. However, at the catchment level it is unclear how much impact the organic matter from trees is having on algal blooms. This research would involve working with the water industry to tackle this question.

Cascading failure attacks against smart grids

Electric power systems are considered critical infrastructure and are susceptible to various contingencies such as natural disasters, system errors, and malicious attacks. These contingencies can have a severe impact on the world's economy and cause significant inconvenience to our daily lives. Hence, the security of power systems has been a topic of considerable interest for decades. With the recent development of the Internet of Things (IoT), power systems can support various network functions throughout the generation, transmission, distribution, and consumption of energy through IoT devices like sensors and smart meters. However, this has also led to an increase in security threats. Cascading failures are one of the most severe problems in power systems and can result in catastrophic impacts such as widespread blackouts. Furthermore, these failures can be exploited by malicious attackers to launch physical or cyber attacks on the power system. This project aims to investigate cascading failure attacks and develop AI techniques to detect and defend against them. Feel free to contact me for further discussion.

Cell transplantation and drug options for peripheral nerve repair

Peripheral nerve injuries are devastating as they can result in permanent paralysis. This project will use drug discovery and cell transplantation approaches to develop therapies to treat peripheral nerve injuries in animal models. The interaction of the transplanted cells with the host nerve will be examined and the functional outcomes will be addressed using behavioural and electrophysiological studies.

Cell transplantation therapy to repair spinal cord injury

Olfactory glial cell transplantation therapy is effective for repairing spinal cord injury, but the approach needs enhancing to improve outcomes. This project will determine the optimal combination of cell types needed to produce cellular nerve bridges for transplantation into the injury spinal cord. The project will develop new techniques for cell purification and three-dimensional cell nerve bridge production.

Chlorine Evolution Catalysts for Efferent Seawater Electrolysis

Seawater is the most abundant aqueous resource on earth that is readily accessible at very low costs, but yet to be directly utilised for production of hydrogen fuel and commodity chemicals. This project aims to develop cheap and plentiful carbon-based high performance chlorine evolution electrocatalysts for seawater electrolysis powered by renewable electricity to realise the production of hydrogen, chlorine and sodium hydroxide directly from seawater. The electrolyser can also be used to treat desalination brine while produce hydrogen and chemicals. The success of the project will set a firm technological foundation for seawater utilisation, which will add to Australian capability to meet future energy and environment challenges.

Combating Giardiasis by investigating new potent compound series as leads for improved treatments

Giardia parasites infect approximately 1 billion people and cause over 200 million cases of giardiasis each year. They also cause significant morbidity in animals. However, current treatments are inadequate, associated with resistance and collateral microbiota impacts. This project aims to improve the treatment of giardiasis by investigating the biological and pre-clinical activity of potent new anti-Giardia compounds in animal models of infection.

Computational Science and Engineering

Computational science and engineering is a modern approach to research, distinct from standard theoretical and experimental approaches. In computational research, fast computers are used to simulate or model the behaviour of physical systems to better understanding properties too difficult or expensive to study via experiments. Nano- and micro-scaled systems can pose a particular challenge for conventional experiments and theory, and are a natural fit for computational study.

Construction of a bioengineered choroid to study regenerative healing

Macular degeneration causes devastating visual impairment in the Australian population, and without new and effective treatment options, one in seven Australians with early signs of macular degeneration will likely progress towards advanced stages of the disease. The earliest known pathophysiological event to occur in macular degeneration is choroidal vascular dropout, and little is known about the events that occur prior to this microvasculature dysfunction and the contribution of the surrounding choroidal stroma and its resident cell populations. This project aims to construct a multicellular bioengineered choroid to elucidate deeper understanding about this specialized sensory support tissue.

Continuous beam atom interferometers for quantum enhanced navigation

Atom interferometers have demonstrated great promise for next generation accelerometers and gyroscopes, with significant gains in sensitivity and immunity to bias drift . To date, most work has focused on pulsed atom interferometers, which use a series of time-seaparated light pulses to split and recombine the atomic ensemble, with the resulting phase shif. However, pulsed approaches suffer from significant loss in bandwidth, due to dead-time where no measurement is made. This project will construct a continuous beam interferometer using laser cooled rubidium atoms, with the interfereterometer sequence constructed by atoms traversing spatially separated light fields, giving significant gains in bandwidth and flux.

Coordination in multiagent swarms

Coordinated action by multiple agents (such in robotic swarms), is an important area, especially whe there is limited or only intermittent communication. This requires both local planning and adjustment when there is a possibility to coordinate, so that the swarm as an emergent agent can fulfill an overall intent. The work would involve literature review, theory building, and validation through simulation experiments (using a multi-agent simulation platform).

Counterfactual quantum smoothing

Quantum state smoothing is a newly developed way to estimate the state of a quantum system at time t using measurement results in both the past and future of t, with applications in experiments with continuous measurements. This project will further develop this formalism, including using it to address the question of what is the most likely thing a quantum system would have done if you had measured it in a different way from how you did. Feel free to contact me about other areas I have published in recently.

Cumulative effects of light and chemical pollution on circadian systems in fish

Human activities introduce a huge diversity of pollutants into the environment, often with harmful consequences for wildlife. These pollutants frequently overlap, but many knowledge gaps exist when it comes to predicting their combined risks. Light pollution and pharmaceutical anti-depressants are two of the fasting growing stressors globally and have both been shown to negatively impact aquatic animals, but there has been no research exploring their interactive effects. This project aims to investigate the combined risks of light pollution and anti-depressant pharmaceuticals on the regulation of circadian systems, at multiple levels of biological organisation. The outcomes are expected to yield a new framework for exploring the interactive effects of chemical and non-chemical stressors and to reveal how non-chemical circadian entrainment cues such as light pollution modulate chemical toxicity.

Cybersecurity in Industry 5.0

As the use of connected devices and the Internet of Things (IoT) becomes more prevalent in manufacturing processes in the Fifth Industrial Revolution (Industry 5.0), cybersecurity becomes a critical consideration. The integration of these devices presents new opportunities for cybercriminals to exploit vulnerabilities and attack the system, hence organisations must implement robust cybersecurity measures to safeguard their data and systems. A significant cybersecurity challenge in Industry 5.0 is protecting the data generated by connected devices. This information is often confidential and valuable, and unauthorised access to it can disrupt operations or result in intellectual property theft. To counter this risk, it is crucial to encrypt, securely transfer and store data to prevent unauthorised access. The project will study the new threat landscape in industry 5.0, and propose mitigation strategies for the new vulnerabilities introduced by the Fifth Industrial Revolution.

Data Privacy Analysis based on AI

Many companies have the privacy policy set for the data they collected. Due to the evolution of AI-based technology, how AI shall be used to help with an automated privacy impact assessment?

Deep Explainability of Multi-View Behavior Interdependence and Heterogenity

This project proposes to develop advanced AI models capable of providing deep insights into complex interdependencies and heterogeneous behaviors observed across multiple data views. By leveraging state-of-the-art deep learning techniques and interpretability methods, the project seeks to unravel intricate relationships and patterns within multi-view data sources, enabling a comprehensive understanding and explanation of how diverse factors interact and contribute to observed behaviors. Through rigorous experimentation and analysis, the project aims to enhance the transparency and interpretability of AI models, facilitating informed decision-making in various domains such as healthcare, finance, and social sciences.

Deep learning-based building roof part segmentation

The 3D object reconstruction is highly challenging due to high data complexity, structural variations, presence of noise, and missing of data. Buildings come in different shapes and their unique architectural designs pose a great challenge, specifically for extraction and modelling of small components such as chimneys. The recent deep learning architectures have shown high success in object-part segmentation, e.g., a plane can be divided into wing, body, fin, and stabilizer. So, prior knowledge about a building roof style can be sought as a prerequisite step for building roof reconstruction. The project aims to employ deep learning architecture to segment a roof into parts and then classify them into roof styles before the roof is reconstructed as a complex shape.

Detecting microplastics in organic-rich materials and their potential risks to terrestrial ecosystems

Microplastics (MPs) are a major emerging contaminant in agroecosystems, due to their significant resistance to degradation in terrestrial environments. This project asseses the characteristics and fate of MPs in contaminated soils and their risks to soil biota.

Developing a Digital Micro-reactor

This project aims to develop non-wetting droplets that can be used for chemical reactions and cell culture. These non-wetting droplets, known as liquid marbles, act as standalone miniature reactors that can be manipulated by external stimuli. This project focuses on using liquid marbles as building blocks for an integrated digital reactor platform that substantially improves reaction rates. You will work with research or commercial users to explore novel solutions as well as design and build innovative devices.

Developing biogeochemical fingerprinting models for tracing the terrestrial source of sediment and nutrient in river systems

Elevated levels of terrigenous sediments in river systems has long been regarded as one of the most deteriorating factors on water quality in rivers and coastal area. However, the land use sources of sediments in rivers systems are uncertain. In this study we will develope novel biogeochemical fingerprinting models for tracing the terrestrial source of sediment and nutrient in river systems.

Developing cost-effective and efficient defective materials for electrocatalysis

This project aims to exploit high-performance, durable and cost-effective defective electrocatalysts for fuel cell and water splitting applications. It expects to generate a new area of knowledge to understand the interfacial phenomena of electrocatalysis and of how to develop technologies for the controllable synthesis of low-cost and highly efficient electrocatalysts. The expected outcomes of this project include a process for the development of cost-effective electrocatalysts thereby making hydrogen fuel cells and water splitting techniques economically competitive.

Developing eXplainable AI (XAI) techniques using automated reasoning

Machine learning is one of the hottest topics in computer science, but it is often used as a "black box"; consequently, the trained model may behave unexpectedly and yield catastrophic results. This project aims to develop new methods to understand machine learning models. We will adopt various techniques that synthesise different forms of "explanations" as approximations of the original machine learning models. We will evaluate these methods for a variety of machine learning algorithms, including CNN, RNN, random forest, and reinforcement learning. As a case study, we will look into modelling the abstract state machines in Process Analysis Toolkit (PAT) and develop an interactive query system that allows the user to ask questions about machine learning models and get answers. We will also investigate how to present the interactive system in a user-friendly interface.

Developing native mass spectrometry methods to charterise disease related biomolecules

This project aims to develop native mass spectrometry methods for characterising challenging and unconventional targets that underpin emerging disease therapeutics. Native mass spectrometry is a rapidly growing biophysical technique – this project is one of few opportunities in Australia to develop skills with this emerging and continually developing methodology. Potential biomolecular targets to be investigated include soluble and membrane proteins and structured RNAs, and their complexes with other proteins, nucleic acids and/or lipid binding partners. Development of these methods will facilitate the fundamental understanding of these molecules and further drug discovery by allowing fragment, or other, screening campaigns to discover novel binding compounds, or characterise previously identified therapeutic binding compounds. This can be applied to various diseases areas including cancer and infectious diseases

Developing novel Soil Ameliorants to improve agricultural activities in drought conditions

We are currently looking for a PhD candidate to work on Soil Ameliorants. The primary purpose of this role is to develop a series of novel Soil Ameliorants from locally available materials or wastes. The ideal candidate needs to have a relevant background in chemistry. Success in this role requires collaboration with research partners, industry and farmers. This PhD project will be based on Nathan Campus, Griffith University.

Developing RNA-based treatment to enhance immune response against cancers

This project aims to investigate the epigenetic regulation via microRNA gene silencing adopted by Epstein-Barr virus (EBV) to “hack” the genetic program of human B-lymphocytes (B-cells). We use a novel EBV/B-cell model system to characterise the functional role of viral microRNAs in the micro-management of cellular pathways associated with persistent B-cell infection. Our integrated platform will contribute to better understanding of fundamental molecular and cellular processes underpinning viral infection, immune escape and proliferation. The overarching goal is to produce a system-based platform to understand the mechanisms of epigenetic regulation by microRNA gene silencing associated with virus-host interactions and human cell infection.

Developing sustainable growth media for bioretention systems using recycled materials

Bioretention systems are excavated basins or trenches that are filled with porous filter media and planted with vegetation to remove pollutants from stormwater runoff.The main aim of this project is to examine the impacts of locally available recycled organic amendments on improvement of plants performance and reduction of nutrient leaching from bioretention filter media. The main objective is to design a cost-effective and functional bioretention filter media with optimum nutrient retention capacity and carbon storage for supporting sustainable plant performance in bioretention systems.

Development of new nucleic acid chemistries

This project aims to develop new nucleic acid chemistries to facilitate functionalisation and improve the biological stability of oligonucleotide therapeutics (ASO, siRNA, CRISPR). These new functionalisation chemistries will be designed to allow fast conjugation and screening of moieties that improve cell targeting, penetration, and metabolism of the oligonucleotide therapeutics. The project will involve the design and synthesis of nucleotide phosphoramidite precursors monomers, the semi-automated synthesis of oligonucleotide sequences, and performing oligonucleotide bioconjugation and functionalisation assays. This project has a strong focus developing industry-ready candidates, creating valuable IP, and impactful publications.

Development of novel nanomedicine for treatment of vascular calcification

Vascular calcification is an actively-regulated process mediated by vascular smooth muscle cells where calcium phosphate crystallizes in the form of apatite, predominately depositing in the vascular tissues. Vascular calcification is one of the predictors of cardiovascular disease and can lead to cardiovascular dysfunction. This project aims to develop novel biomimetic-functional nanomaterials for targeted treatment of vascular calcification. This intelligent material is expected to specifically reach VC site where it releases a local anti-calcification activity, which could minimise off-target side effect and enhance therapeutic capability with minimal administered dose.

Discovering macroalgal diversity in the Great Barrier Reef

Macroalgae or seaweeds are a fundamental component of the Great Barrier Reef, but their diversity is poorly known. This project aims at discovering and documenting the diversity of marine benthic algae using molecular methods for a better understanding of their natural history and roles in coral reefs.

Discovery of new anti-infective drugs from the sea

Nature provides unlimited inspiration for innovation in the pharmaceutical and agrochemical sector. The Nobel Prize-winning discovery of the anti-parasitic drugs avermectin and artemisinin has renewed interest in exploring natural products for new anti-infective drugs. This project will result in the identification, semi-synthesis and full characterisation of new molecules that display anti-viral, anti-microbial or anti-parasitic activity.

Disruptive technology in natural products chemistry: rapid discovery of natural product-based probes

This project aims to develop new approaches for the identification of novel natural products (purified or within a fraction library) interacting with known (target-biased strategy) or unknown (unbiased strategy) protein targets based on advanced mass spectrometry (MS) techniques. An innovative and powerful chemical biology platform will be established to enable direct and rapid discovery of natural product-based probes and their protein targets. This project detects NP-protein interactions directly from cell lysates treated with natural product extracts/fractions/compounds.

Drinking water security in Australia under climate change 

Drinking water supply is fundamentally influenced by climate. As climate change occurs, potentially causing longer duration of droughts and more frequent storm events, it is essential to assess how it will affect our drinking water security. This project will use recent updates to climate change datasets and hydrological models to assess drinking water security across Australia

Drug discovery to treat Alzheimer's disease

Pathogens such as bacteria and viruses are likely contributors to the onset and progression of Alzheimer’s disease. This project will use drug discovery to identify compounds that can stimulate glial cells of the nervous system to combat chronic pathogen infection of the brain. The project will use in vitro cell cultures and in vivo animal models of brain infection.

Ecological connectivity through fish movement measured using artificial intelligence

Connectivity is a guiding principle for conservation planning, but due to challenges in quantifying connectivity, empirical data remain scarce. This project provides solutions to the challenge by using computer vision to automatically extract fish movement data from underwater camera streams. The student will develop expertise in fisheries ecology, statistical modelling and programming. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams. It will lead to better planning and management of marine restoration and protected area projects through incorporation of connectivity principles.

Ecological connectivity through fish movement measured using artificial intelligence

Connectivity is a guiding principle for conservation planning, but due to challenges in quantifying connectivity, empirical data remain scarce. This project provides solutions to the challenge by using computer vision to automatically extract fish movement data from underwater camera streams. The student will develop expertise in fisheries ecology, statistical modelling and programming. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams. It will lead to better planning and management of marine restoration and protected area projects through incorporation of connectivity principles.

Ecology of coral reef ecosystems

Coral reefs are complex ecosystems but are under threat from anthropogenic activities. When reefs degrade, corals are normally replaced by macroalgae, therefore understanding macroalgal ecology is critical for the conservation of the Great Barrier Reef (GBR). This project aims at providing fundamental knowledge of the ecological processes involved in macroalgal blooms in the GBR.

EEG Biometrics

Electroencephalography, or EEG for short, is a technique that measures the electrical activity of the brain through electrodes placed on the scalp. This non-invasive and cost-effective technique has been used in various fields, including neuroscience research and clinical practice. One of the main reasons why EEG research is so important is because it allows us to gain valuable insight into brain function and dysfunction. It has been extensively used to investigate a wide range of cognitive processes, such as attention, perception, memory, language, and emotion. Furthermore, EEG has been instrumental in diagnosing and monitoring neurological disorders, including epilepsy, sleep disorders, and traumatic brain injury. Overall, EEG research is an essential tool for understanding the human brain and its disorders. This project focuses on research on EEG biometrics and its applications.

Effect of fire and global warming on soil organic carbon in different ecosystems

Changes in fire regime and global warming are significant and interactive symptoms of climate change. In this study we would like to investigate the long-term, interactive impacts of fire and warming on soil C dynamics and soil-to-atmosphere C fluxes in different ecosystems

Electrochemistry in nanoconfinement to improve Li ion transport in batteries

This project focuses on understanding how nanostructures affect electrochemical reactions. More specifically, this project aims to understand how electrochemistry in nanoconfinement affects Li ion transport to improve the performance of Li-batteries

Enabling high-performance wearable energy storage devices through additive manufacturing

This project aims to design and develop functional nanomaterials and nanocomposites for high-performance wearable energy storage devices (WESDs). A functional materials approach, together with precise control of device architecture through multi-materials/techniques additive manufacturing will be used to achieve maximum device performance with the required mechanical properties. The expected outcomes of this project include a detailed understanding of materials and devices structural-property relationship and the establishment of the fundamental principles on the microfabrication of flexible energy storage devices to support the burgeoning field of wearable devices, thus advancing the field of materials chemistry and advanced manufacturing.

Enacting 'procedures as resources for action' approach through new technology and IT system

High risk industries reliance on procedures is high; there are checklists, memory items, procedures, manuals and rules that direct how a cockpit should be configured, what to do in an emergency and whether an aircraft can take off given the physical environmental conditions. Despite their relevance, the number of procedures and rules is increasing every year without a direct translation into a reduction in the number of accidents and incidents. As an alternative to the current approach to procedures, which are seen as the only way to create safety, resources for action see procedures as a supplement to the activity. It provides the information required to complete a task if and when the worker needs it. However, how do procedures as resources for action look like in practice? In this research project, we aim to develop normal and abnormal situations checklists sensible to the context that provide the information needed, when needed, if needed.

Engineering micropatterned surfaces for cell mechanics and mechanobiology.

This project aims to engineer a highly versatile micropatterned surface that can be used to culture and study cells.

Engineering semitransparent perovskite solar cells for smart solar windows

This project aims to develop highly efficient and stable semitransparent perovskite solar cells for innovative smart solar windows. The key concept is to explore novel functionalisation strategies on emerging carbon and two-dimensional materials to fabricate semitransparent perovskite solar cells for self-powered smart photovoltaic windows. Expected outcomes of this project include not only placing Australia at the forefront of research in the fields of materials science and renewable energy, but also creating commercial opportunities in Australia. This project expects to have various benefits for Australians – through the development of a cutting-edge sustainable energy device and the establishment of strong international collaborations.

Enhancing Cyber Security of Cyber Physical Systems through Digital Twins

The progress made in fields such as the internet of things, artificial intelligence, machine learning, and data analytics has facilitated the development of digital twin technology. A digital twin is a high-fidelity digital model of a physical asset or system that can be utilised to optimise operations and predict faults of the physical system. Operators of cyber-physical systems need to be aware of the cyber situation in order to adequately address any cyber attacks in a timely manner. Early detection of cyber threats can quicken the incident response process and mitigate the consequences of attacks. However, gaining a complete understanding of the cyber situation may be difficult due to the complexity of cyber-physical systems and the ever-changing threat landscape. More specifically, cyber-physical systems (CPSs) usually have to be continuously operational, and they may be sensitive to active scanning of the network traffic. Digital twins can address these challenges by providing virtual replicas of physical systems that can be analysed in-depth without disrupting operational technology services. This project aims to assess the usefulness of digital twins for the cybersecurity of cyberphysical systems and review the tools and technologies available for creating them. Additionally, a cybersecurity framework for anomaly detection using digital twins in cyber-physical systems will be proposed.

Entropy-tuned alloys for hydrogen storage

When metals absorb atomic hydrogen from molecular hydrogen gas, first a disordered solid solution and then an ordered concentrated hydride phase are formed, with evolution of heat (enthalpy). The entropy and enthalpy changes are fundamentally linked through statistical mechanics. The goal of this PhD project is to control the enthalpy of the hydridring reaction by controlling the entropy change, with relevance to hydrogen storage (low enthalpy is desirable) and metal-hydride hydrogen compressors (high enthalpy is desirable). This would desirably involve both theoretical modelling using Density Functional Theory/Calphad to explore the possibilities of designing alloys such that the solid-solution phase has significantly higher/lower configurational entropy than the concentrated hydride phase, and experiments to make small amounts of alloys and measuring their hydrogen uptake properties in the National Hydrogen Materials Reference Facility within QMNC).

Environmental impact of firefighting chemicals and bushfire leachates on aquatic ecosystems

The project will investigate the fate and effects of firefighting chemicals and bushfire leachates in Eastern Australian waterways to assess the risk they pose to aquatic organisms and ecosystems on the short term and long term. Firefighting chemicals are deployed by emergency services for the protection of life and property, however there is a gap in the knowledge associated with their short- and long-term effects to water quality and aquatic ecosystems. This will be a largely lab-based experimental project and will aim to better understand if and at what scale these chemicals impact aquatic ecosystems and the timescales associated with these potential impacts. Other lines of evidence will also be explored such as the identification of ‘signatures’ associated with firefighting chemicals to better understand the contribution they have to water quality impacts in a large severely burnt catchment. This project is a collaboration between the NSW Government’s Estuaries and Catchment team based in Lidcombe NSW and Griffith University (Gold Coast campus) with opportunities to work across each location. Focus areas are bushfire-related aquatic ecotoxicology, environmental pollution, and environmental chemistry.

Evaluating the role of ecosystem processes in enhancement of soil carbon stocks and functional resilience

this project aims to improve our understanding of how ecosystem processes affect soil carbon quality and quantity, and how this in turn influences soil resilience to environmental stresses (e.g. drought, compaction, chemical residues of fungicides, and carbon decline) and to develop sensitive and affordable assessment protocols for improvement of soil carbon stocks and functional resilience to environmental stresses.

Expanding research on Blockchain (DLT) for patient privacy

Pre-existing research has failed to offer a solution to protect patients’ privacy and confidentiality, it is important to identify the limitations of existing solutions and envision directions for future research in privacy preservation in health informatics. This research aims to identify current outstanding issues that act as impediments to the successful implementation of privacy measures in health informatics and the limitations of available solutions. Feasibility of using blockchains for dealing with health and medical will be researched and evaluated. Then, propose a privacy-preserving framework by improving data storage, record linkage techniques.

Expansion and erosion of amphitheatre gullies - a modelling study

Gullies are the majority source of sediment discharged into the Great Barrier Reef, motivating significant investment to prevent erosion and improve water quality in receiving environments. Large amphitheatre gullies are complex structures with highly variable erosion processes. Process-based models are required to inform rehabilitation practices, and to inform investment at the catchment scale. This project will develop models of gully erosion suitable for informing management in ampitheatre gullies. This project will involve collaboration with Queensland Government and the Queensland Water Modelling Network and is associated with an ARC Industry Fellowship.

Explainable machine learning in intrusion detection systems

Over the past few years, many intrusion detection systems (IDSs) have been developed using machine learning methods. These automated IDSs can automatically analyse network data, including network traffic and device logs, to detect intrusions. Cybersecurity experts rely on these systems' recommendations to improve network security. To enhance the reliability of IDSs, it is important that the decisions made by these machine learning-based solutions can be justified to humans. However, the current automated IDSs are used as black boxes, providing no information about the reasons behind their predictions. It should be clear to cybersecurity experts which features of the network data caused the intrusion. This project aims to identify state-of-the-art techniques to develop an explainable IDS, addressing this gap and providing a better explanation of IDS decisions. It will investigate how existing methods can be improved to provide more comprehensive interpretability of machine-learning-based IDSs and provide details about the features involved in IDS decisions.

Exploring new biological targets for treating Parkinson’s disease – from patients back to patients

Genetic factors constitute a major component in the aetiology of Parkinson's disease (PD). Significant progress towards understanding the pathologic mechanisms involved in PD and developing new therapeutics has come from studies of rare families with inherited PD. We hold an advantaged position in this research field via access to the unique cohort of thousands of PD patients participating in the Queensland Parkinson’s Project. Through sophisticated genetic studies, we have identified several novel genes from rare PD families, the encoded proteins of which have great potential in elucidating new pathologic mechanisms and providing novel treatment strategies. Using methods in molecular biology, cell biology, biochemistry and stem cell biology, we aim to shed new light on this progressive and devastating disease.

Exploring the immuno-modulatory effect of fungal beta-glucans

Mushrooms are increasingly attracting attention for their immuno-modulatory activities, which are primarily due to beta-glucans. beta-Glucans comprise a group of glucose (Glc) polysaccharides that are chemically diverse, with a common b-glucan being cellulose (b-(1,4)-linked Glc. It is non-cellulosic beta-glucans, mainly beta-(1,3)-linked Glc that have been shown to be potent immunological stimulators in humans, and some are now used clinically in China and Japan, as well as being commercially available in Australia. Due to the complexity of beta-glucan chemistry and structure a detailed understanding of the mechanism of action, specifically the structural components that dictate specific immunological responses, are yet to be fully resolved. In collaboration with Integria Healthcare, the overall objective of the project is to explore the immuno-modulatory effects of mushroom beta-glucans, specifically the project aims to structurally characterise commercially available mushroom polysaccharides rich in beta-glucans and correlate this with their associated immuno-modulatory effects The outcomes from this project will lead to a clearer understanding of the properties of beta-glucans associated with commercially available mushroom polysaccharides that induce specific immuno-modulatory effects.

Extraction and fault assessment of powerline components

Essential powerline components such as conductor, cross arm and insulator will be periodically extracted and their properties (narrowing of diameters, broken discs, sizes, material fatigue) will be estimated using machine learning techniques combined with a statistical analysis. Also, faults in these components will be automatically detected and managed. Along with point cloud data, multispectral, hyperspectral as well as thermal imagery could be used for these purposes.

Fake News Detection based on Large Language Model

This project aims to develop an advanced system for automatically identifying and flagging fake news articles using large language models. Leveraging the capabilities of large language models like GPT, this project involves preprocessing a diverse dataset of news articles, extracting meaningful features, and training a classifier to distinguish between genuine and fake news. This project will explore fine-tuning techniques to enhance the model's adaptability to different domains and evolving forms of fake news. The ultimate goal is to deploy a robust fake news detection system capable of assisting in the ongoing battle against misinformation and safeguarding the integrity of online information dissemination.

False alarm detection and removal through contextual reasoning for Cyber Security

In today’s digital ecosystem world, we see more and more intelligent devices are connected over the Internet, enabling them to share their data on the Web. This allowed us to collect a large amount of data and use then for intelligent situation awareness and intrusion detection. This research will address the key issues facing to cyber security: 1) big streaming data analysis, 2) the huge number of generated alarms with the vast majority being false alarms, 3) human effort to investigate alarms to find intrusions, 4) determination and removal of false alarms, 5) timely decision making in a constantly changing environment, and 6) the ability to capture previously unknown attacks.

Fast Approximation algorithms for subgraph queries

Graph data are ubiquitous nowadays. Real-world graphs (e.g., social network graphs, knowledge graphs, road networks) are getting larger and larger, which makes many common graph queries (e.g., subgraph matching/counting, crucial nodes/edges identification, cohesive subgraph computation, constrained shortest paths) time-consuming. However, in many real-world applications, approximate answers are sufficient and much easier to find. This PhD project aims at developing novel techniques for the fast finding of approximate answers, focusing on subgraph computation/counting queries.

Forensic Applications of Nanoscience

Developing and applying nanotechnologies to deliver solutions to forensic problems. Broadly speaking, these activities seek to apply materials science in a forensic context. Key areas of focus include: new fingermark development strategies; improving the specificity of presumptive testing for drugs of abuse; and assessing new and overlooked classes of evidence. Key themes include: safer, greener forensic processes; delivering new functionality or clearer interpretation; and interdisciplinary, practitioner-informed research.

Forgetting while fine-tuning Large Language Models

The popularity of OpenAI ChatGPT revolutionised the GenerativeAI. While these models provide huge benefits training such models is time-consuming and costly, which causes the information in such models to be not up to date. Proposed methods to finetune smaller base LLM with up-to-date data do not necessarily discard irrelevant outdated data from Large Language Models. This work will look into options to ensure finetuning of the models forgets only desired parts and does not cause catastrophic forgetting due to multiple model fine-tuning.

Formal modelling of the semantics of programming languages

This project investigates the semantics of popular programming languages at different levels. For instance, we will formally model the semantics of a high-level programming language, such as rust, in a formal verification tool, such as a theorem prover, to understand exactly how a piece of code works and whether it is correct with respect to the user specifications. We will also model program semantics at a lower level (e.g., compiled binary code) and check whether low-level semantics conforms with high-level code. The formal modelling of the programming language of choice should lead to verification tools with practical impact in the industry.

Formulation and application of deep learning and other AI techniques to physical sciences

The current wave of deep learning and AI research has yielded many advances in how tools such as neural networks, optimization, or uncertainty quantification can be used to improve modelling capability for a number of useful applications. Projects are available in the development of robust and transparent machine learning and AI techniques that can be employed to augment existing computational modelling techniques (e.g. surrogate models, reduced order models, etc) or to provide new avenues of solution (e.g. PINNs as a famous example).

From Local Search and Complete Search to Machine Learning and Back

This Project aims to investigate the mechanism that integrates local search and complete search, and machine learning for real-world applications. This project will develop the strategies for the cooperation of local search and complete search in solving hard problems from real-world. It will explore the cooperation of local search and complete search for training deep neural networks. On the other hand, this project will propose novel mechanism to design local strategy to by using machine learning technologies. The aims of this Project include both the novel paradigm for training deep neural networks and efficient algorithms with the cooperation of local and complete search strategies.

From trapping a single cell to unravelling DNA

Life is the dynamics of large biomolecules. This project aims to develop a novel experimental approach to achieve atomic levels of control over large biomolecules through manipulation of electrostatically levitated bioparticles in a Paul trap. This starts with single yeast cells and will progress to developing laser and electron optics techniques to controllably fragment the cell into organelles and into isolating single chromosomes. These chromosomes will then be controllably and potentially reversibly unfolded using single electron changes in the static electrical charge to demonstrate an atomically resolve force microscopy. A Gold Coast based joint with the Institute for Glycomics

Fundamental thermodynamics of metal - hydrogen systems

Three-quarters of the periodic table is metals, and essentially all of these can be made to absorb hydrogen to form "alloys" (e.g. PdH) and compounds (e.g. MgH2). Thousands of metallic alloys also absorb hydrogen. In very many cases, hydrogen first forms a dilute solid solution which, as the hydrogen gas pressure increases, becomes unstable and a phase transformation to a concentrated hydride phase occurs, up to the thermodynamic critical point of the system. The goal of this PhD project is to investigate some new ideas about hydrogen uptake by metals. Two of these are: (i) Recent theoretical work based on Density Functional Theory proposes that in nanoparticles the system can transform via a single phase even below the critical point. This published result is controversial and has not been proved experimentally. This proposed phenomenon will be investigated in the Pd - D2 system by measuring hydrogen uptake while performing neutron diffraction (at the Australian Neutron Scattering Centre in Sydney) to determine what phases are present. (ii) Recent analysis based on statistical mechanics predicts that the assumed linear relationship between log(absorption pressure) and reciprocal temperature (the van 't Hoff relation) is in fact curved at high pressure, which matters for applications such as hetal-hydride hydrogen compressors for vehicle filling stations. This published result will be tested by measurements of hydrogen uptake by alloys at pressures up to 2000 atmospheres.

Fusion of symbolic and sub-symbolic computation (towards AI that displays general intelligence)

Current AI/ML techniques are limited in terms lacking meta-cognition that allows a system to reason about its own abilities and capabilities in light of the problem space encountered. The project would suit a candidate who is interested in both the theory of AI and in experimenting with implementation tools to build efficient and effective AI systems (e.g. SOAR, CLARION, ACT-R,...).

Gender equality in Aviation - From academia to industry boards

The aviation industry is often seen as a symbol of globalisation, connecting people and businesses worldwide. However, despite its global reach, the industry has been slow to address issues of gender inequality. Women have been historically underrepresented in aviation, from academia to industry boards. This has led to a lack of diversity in leadership positions and a culture that can be unwelcoming to women. In recent years, there has been a growing recognition of the importance of gender equality in aviation. From initiatives to increase the number of women in pilot training programs to campaigns to promote diversity in leadership roles, the industry is taking steps to create a more inclusive environment. Despite the progress that has been made, there is still a long way to go to achieve gender equality in aviation. By continuing to push for change and challenging the status quo, this research aims to explore avenues that will lead to a more inclusive future for aviation.

Gene networks associated with neurological disorders: the key to better diagnostics and treatments

Neurological disorders such as schizophrenia and dementia are caused by a ‘perfect storm’ of unique combinations of genetic and environmental factors. Such complex combination of events leads to disruptions in gene networks and biological pathways that alter cell functions and consequently influence disease risk. New approaches in genomic technologies, computational models and experimental systems could potentially lead to personalised treatment based on an individual’s genetic composition. This project aims to map molecular networks and cell functions affected in patient-derived stem cells to help discover new therapeutic strategies tailored based on patient’s molecular and cellular signatures.

Generating and sharing cyber threat intelligence in industrial control systems

Cyber threat intelligence (CTI) is the knowledge about a threat, and it includes threat indicators such as Tactics, Techniques, and Procedures (TTPs), IPs, etc. CTI can help organizations to learn about existing threats. Cyber threat intelligence can be received from external open-source threat intelligence, or it can be extracted from adversarial activities in organizations’ networks. The CTI generated will be used to build intelligence about threats against a given target. In cyber threat intelligence, indicators of compromises (IoCs) are generated. These IoCs of the detected adversary can be processed and distributed. Security analysts to use CTI information from other companies and share back their IoCs with other trusted partners. These shared IoCs can be used to update detection rules and blacklists in security devices like firewalls. This project will review state-of-the-art techniques CTI sharing and identify gaps in the current solutions. It will also investigate how threat intelligence can be automatically created and shared for new emerging attack and link the CTI to cyber security defence mechanisms.

Generating high-quality reference genome assemblies for Australian Papaya varieties

We are currently looking for a PhD candidate to develop genomic resources and tools for Australian papayas to facilitate future smart breeding of elite varieties. The primary objectives of this role are to: (a) sequence and annotate the reference genomes of selected Australian papaya varieties and (b) develop high-density genetic markers for Australian papaya and wider germplasm collections. The goal is to then uncover genomic sequences that may be used for accurate selection of preferred flavour and productivity traits across a broad germplasm set. Outputs from this project will directly contribute to genomic prediction approaches for developing elite papaya varieties. This project includes molecular, genomic and transcriptomic approaches that will leverage prior knowledge and skills developed in our group and by collaborators. These include high density SNP mapping and QTLs underpinning several fruit quality traits and possible gene candidates, as well as trained sensory panel and biochemical profiling performed to identify volatiles and other compounds that are associated with distinct fruit flavours. Success in this role requires collaboration with fellow team members and leading researchers from the University of Queensland, Murdoch University and the Queensland Department of Agriculture and Fisheries.

Genetic toolkits to preserve biodiversity and functions in coastal and marine ecosystems

Australia's expansive coastal and marine ecosystems are in dire need of improved biological monitoring to preserve their valuable and unique biodiversity in the face of human-related disturbances. This Project responds to the challenge by upscaling and revolutionising fit-for-purpose genetic toolkits that can extract whole ecosystem DNA data from environmental samples. This innovation will be done in the interest of answering previously inaccessible ecological questions related to biodiversity, supporting habitat restoration and engineering solutions, complementing rather than replacing existing biological monitoring, supporting commercial outcomes with automation, and benchmarking the health of coastal and marine ecosystems under threat.

Geochemistry of critical metals in coastal and marine waters

Australia is home to large reserves of "critical minerals" - those metals that are essential to the transition to renewable technologies. Our knowledge of the environmental chemistry of these metals is currently limited, particularly in the coastal and marine waters that will likely be their ultimate sink. This project seeks to use advanced analytical approaches, including Synchrotron-radiation X-ray spectroscopy, to unravel the complex aquatic geochemistry of critical metals in coastal and marine environments.

Graph Neural Networks for Drug Discovery

Graph neural networks (GNNs) are emerging techniques for AI. As many chemical compounds and proteins in biology can be modelled as graphs, GNNs have great potentials for drug discovery. This research will investigate new GNN based techniques to accelerate the process of drug discovery.

Graphical program analysis

Through previous work with Prof Bernhard Moller and Turing Award Laureate Sir Tony Hoare, we proposed a geometric theory for program analysis in which a computer program is represented by dots and lines in a diagram. Prof Moller has laid out the theoretical foundation of the work, and we are now ready to proceed into a more practical development. Our vision is to build a program analysis tool with Graphical User Interface (GUI) that supports writing and modelling programs by drawing diagrams and automatically translating a diagram into an executable program. A diagram can also be converted to a Communicating Sequential Process (CSP) model in our tool Process Analysis Toolkit (PAT) and be used for model checking. The outcome is a toolchain that supports user-friendly program analysis, testing and verification.

Greenhouse gas emissions from wetland disturbance

Wetlands can accumulate large amounts of carbon, but when disturbed, this carbon can be released into the atmosphere as CO2 and CH4, contributing to global warming. This project aims to determine how disturbances, including hydrological modifications, feral animals and deforestation, affect the carbon cycle of wetlands (mangroves, marshes and supratidal forests) and how can these be reversed.

Harnessing the power of the macrophage: innovative anti-cancer drugs known as 'maca-attackers'

Despite the massive potential of pharmacologically harnessing the power of the macrophage (MØ), a lack of understanding basic molecular mechanisms led to a distinct absence of MØ-based anti-cancer therapies. MØs are powerful orchestrators of the response to tumours, making up to 50% of tumour mass. The MØ powerfully exerts tumour inhibition via either cytotoxic M1-MØs, or tumour promotion via the M2-MØ phenotype. However, a unifying model of how this occurs via nitric oxide (NO) has never been elucidated. Using our expertise in exploiting transporter pharmacology to develop innovative drugs from bench-to-bedside, we will assess the transporter, multidrug resistance-associated protein 1 (MRP1), to exploit NO transport between MØs and tumour cells (Figure 2) to develop frontier drugs (“MACA-ATTACKERS”) to harness the immense power of the MØ.

High-temperature superconductivity in palladium deuteride

Today's room-temperature superconductors are all metallic hydrides. Superconducting transition temperatures (Tc) now reaching, even exceeding, room temperature are however observed only under extreme pressure, above 1 million atmospheres. Palladium hydrides - PdH, PdD and PdT - have been known to superconduct below about 10 kelvin for 50 years. It was recently found that PdH and PdD can become superconducting after absorbing hydrogen at a temperature and pressure above the thermodynamic critical point of the Pd - H2/D2 system, with superconducting transition temperatures reliably reaching 60 kelvin in PdDx (where x is not yet known). While this is low compared to room temperature, only low hydrogen pressures are required. The goal of this PhD project is to conduct cryogenic experiments to measure Tc of PdDx by by means of resistivity and alternating susceptibility simultaneously, under various experimental conditions, and to thereby understand how to obtain the highest possible Tc for this system.

High-throughput Double Emulsion Using Microfluidics

Double emulsions, referring to droplets of the disperse phase containing even smaller ones, are highly desirable for applications in drug delivery, food science, release of substances, etc., due to the embedded structure which can encapsulate different types of molecules. This project aims to produce and control high-throughput double emulsions using microfluidics.

Hunting for new bioactive natural products from Australian rainforest and desert plants

Humans have utilised plants since the dawn of time for therapeutic purposes. Many important and well-known drugs (e.g. taxol, morphine) come from plants. Endemic Australian rainforest and desert plants have yielded many new and bioactive natural products, but remain under-investigated. This project will result in the purification and characterisation of new bioactive compounds, and that will impact biodiscovery.

Hyperspectral video processing

Hyperspectral videos contain rich spectral, spatial and temporal information of moving objects. The goal of this project is to develop fundamental hyperspectral image analysis, object detection and tracking methods and explore their applications in agricultural, environmenal, and medical applications.

Identification of a vessel wall directed therapy to treat heart disease

The cells of blood vessels produce sticky molecules called proteoglycans and once modified can bind and retain cholesterol. Zebrafish express all the major receptors, lipoproteins and enzymes involved in atherosclerosis and a complete set of genes to proteoglycan synthesis and modification. This project will develop a high-fat diet-induced zebrafish model of atherosclerosis to allow for screening of potential vessel wall directed therapies to prevent cholesterol binding.

Identifying biomarkers for Parkinson’s disease as a step toward a cure

Parkinson’s disease (PD) is a complex, incurable, multifactorial neurological condition affecting over 65,000 Australians with an economic burden of $10 billion per annum. With an aging population the disease related costs will rise unless we find better ways to identify those at risk, provide early diagnosis and treat the disease from an understanding of its causation in each individual. The development of robust biomarkers is essential to meeting these challenges. No biomarkers are available which is the major impediment to progress towards a cure. We have developed a cell model of PF using patients’ own cells. Subjecting the cells to chemical stress reveals a different response between cells from PD patients and those from healthy individuals. We have several projects examining how we can use these stress tests to identify the underlying disease trigger in each patient. This is the first step toward personalised medicine for PD.

Identifying new compounds active against Trichomonas Vaginalis

Trichomoniasis is a neglected parasitic disease that causes significant morbidity in pregnant and elderly women (over 100 million infections each year). However, the only FDA approved therapy for this disease is associated with treatment failures and adverse effects. This project aims to develop and implement a new medium to high-throughput assay to identify and investigate new drug leads for trichomoniasis.

Impacts of bushfires on water quality and biogeological processes in Eastern Australia

The project will assess the impacts of bushfires on water quality and biogeochemical processes within Eastern Australian waterways to better understand the short- and long-term impacts of bushfires on aquatic biogeochemical cycles in estuaries. The fate, transport, and cycling of target metals and nutrients (Fe, Mn, C, N, P, S) will be the focus of this study with both laboratory and field-based experiments utilised. This project is a collaboration between the NSW Government’s Estuaries and Catchment team based in Lidcombe NSW and Griffith University (Gold Coast campus) with opportunities to be based at either location. Focus areas are bushfire-related aquatic biogeochemistry, environmental pollution, and environmental chemistry.

Impacts of disease and environmental contaminants on wildlife conservation

Wildlife are in peril due to numerous threatening processes. Amphibians (especially frogs) are the most endangered Class of Veterbrates. Two key threats to amphibians are disease and environmental contaminants. The main disesae of concern is the fungal disease chytridiomycosis (pronounced 'ki-tri-di-o-my-co-sis'). This disease is caused by two related fungi: Batrachochytrium dendrobatidis and B. salamandrivorans. It is the most devastating disease threat to biodiversity ever recorded. To date it has caused the decline and/or extinction of hundreds of frog species around the world. Another key threat to amphibians (and other aquatic fauna) are environmental contaminants (including pesticides, heavy metals, firefighting chemicals, etc.). I have PhD opportunities available to study (1) infection dynamics of chytridiomycosis in frogs, exploring mechanisms of resistance and tolerance to the disease; and (2) independent and interactive effects of multiple threats to frogs, including the disease chytridiomycosis, and environmental contaminants.

Improving the accuracy of GenerativeAI with fine-tuning of base LLM with domain specific data

While supervised learning is known for a while introduction of pretrained transformers reduced the demand and therefore the cost for annotation of training data. Pretrained transformers learn from large amounts of unlabeled text data and are a form of Large Language Models (LLM). Another milestone in AI was the introduction of the Generative Pretrained Transformers (GPT) framework, which has a decoder layer and is able to understand and generate human-like text. The popularity of OpenAI ChatGPT revolutionised the GenerativeAI. While these models provide huge benefits training such models is time-consuming and costly, which causes the information in such models to be not up to date. For example, popular ChatGPT3.5 was trained with data generated prior to January 2022. There are approaches to finetune smaller base LLM with domain-specific data, however, further improvements are needed to improve accuracy, reduce hallucination and ensure information generated from such models is up-to-date.

Incorporating decision-maker preferences for dynamic multi-objective optimisation

Many real-world problems have multiple, conflicting objectives and/or constraints that are dynamic in nature. These problems are reffered to as dynamic multi-objective optimisation problems. Nature-inspired population-based algorithms enable natural parallel search for a set of optimal trade-off solutions. This study will investigate how a decision maker's preferences can be incorporated in the dynamic search, to focus the search around more preferred regions of the optimal trade-off solution set in an interactive and dynamic way. The algorithms will be applied to disaster management and recovery.

Interaction of attosecond light pulses with atoms and molecules

This project is an investigation into the time that it takes for an electron to tunnel-ionise from molecules, referenced to the tunnelling time from atomic hydrogen The proposed research is based around a state-of-the-art laser system, the Australian Attosecond Science Facility (AASF). This laser system is unique in Australia and one of only a few around the world. The light pulses generated by this laser are highly amplified and are only a few cycles of the optical field, and so measured in attoseconds (10-18 sec). We study of the interaction of such strong-field engineered light pulses with matter. The research will build on a ground-breaking research into the time it takes for tunnel ionisation to occur in atomic hydrogen, which was recently published by the Griffith team in Nature [Sainadh et al. Nature, 568, 75 (2019). This project will extend the measurement of the tunnel ionisation of electrons from other atoms and molecules and will provide the most stringent tests to current models for these interactions.

Interoperable Blockchain Systems for Smart Applications

Blockchain is a promising technology towards achieving full-scale digital transformation in a complex environment. This technique has attracted a number of successful applications such as cryptocurrency, supply chain, trade finance and smart contracts. Blockchain has been showcased as a game changing national technological strategy in several countries. This project will extend our current work on the classification of digital assets, cross-chain integration protocols, and formal verification of smart contracts with novel design patterns and formal security guarantees for inter-blockchain systems. Experiments and validation will be carried out on test-networks and using real-world case studies with our industry collaborators

Interpretable learning in biomedical image analysis

Due to the “black box” characteristics of the deep learning technique, the deep network-based computer-aided diagnosis systems have encountered many difficulties in practical application in healthcare. The crux of the problem is that these models should be explainable – the model should give doctors rationales that can explain the diagnosis.The objective of this project is to research on highly interpretable algorithms to generate "trust" between the human users and the algorithm, designing user-friendly explanations and developing comprehensive evaluation metrics to further advance the research of interpretable machine learning in biomedical image analysis.

Investigation of cyclization - blocked proguanil analogues for malaria

We have made the exciting discovery that the clinically used antimalarial drug proguanil has much more potent activity than previously thought. The activity of proguanil has, up until now, been thought to be due to its in vivo cyclization metabolite cycloguanil, a DHFR inhibitor, and by potentiating atovaquone activity. In this project, cyclization blocked analogues of proguanil, will be investigated as potential new combination partners for atovaquone. Approaches will include in vitro growth inhibition assays, combination studies, time of kill assay and in vivo efficacy testing in murine models of malaria.

Is New Guinea the missing link for understanding Australia's rainforests

We are offering two Ph.D scholarships for motivated students to work on patterns of species richness and turnover across Australasia, with emphasis on drivers of biotic interchange between Northern Australia and New Guinea. Depending on background and interests, students will have research, travel/fieldwork funds to support work on projects such as (a) diversity and systematics of key groups of frogs or lizards, or (b) broadscale projects on patterns and processes of biotic turnover, quantification of biodiversity hotspots, and implications for conservation.

Is nutrient offsetting a win-win for restoring our rivers and coasts?

Nutrient offsetting provides a market based mechanism for restoring catchments to improve the water quality in rivers and the coasts. Point source polluters pay to restore non-point source pollution in catchments. However, there are significant gaps in knowledge in comparing point and non point sources of nutrients in terms of how they affect the environment. This project will work collaboratively with industry and government to examine these nutrient sources and link them to nutrient responses in the environment.

Knowledge Empowered Decision Making in Medical Data Processing

This project focuses on developing explainable AI solutions to decision support systems, by combining knowledge graphs, machine reasoning and machine learning. Knowledge graphs is a promising data and knowledge organisation, synthesis and management approach, and we have developed scalable reasoning tools for knowledge graphs coupled with ontological rules that describe domain knowledge or business rules. This project aims to study the problem of incorporating such high-level knowledge and formal reasoning in the analysis of cross-media data. Moreover, such knowledge and reasoning can be integrated with machine learning models to provide powerful support for informed decision-making where a justification or explanation of the decision can potentially be retrieved.

Knowledge Graph Reasoning at Scale

Knowledge graphs are important tools to enable next generation AI through providing explanation for different applications such as question answering. Knowledge graphs are typically sparse, noisy, and incomplete. Knowledge graph reasoning aims to solving this problem by reasoning missing facts from the large scale knowledge base. This project aims to develop novel scalable technique for knowledge graph reasoning. The developed techniques will be further generalised to more general graphs with graph neural networks.

Koala face recognition

AI-based human face recognition is a mature technology and has been adopted in many applications, such as mobile phone. However, recognition of animal face is still an under-investigated topic. Leveraging the success in human face recognition technology, this project aims to develop novel koala face recognition methods based on transfer learning using images and videos captured in the natural environment. The research outcome will leave to innovative tools for koala population estimation and conservation.

Land-based impacts on marine restoration efforts

Land-based run-off is one of the greatest threats to marine ecosystems, following climate change. Marine restoration efforts are ramping up due to global initiatives and local success stories. While restoration is needed, it is also crucial to understand and elimate threats that degraded land and seascapes to begin with. This project will assess the potential risk of land-based run-off of marine restoration and prioritise areas to focus future efforts.

Land-based run-off in international conservation agreements

Land-based run-off is one of the greatest threats to marine ecosystems, following climate change. However, it is largely ignored in international agreements. Those that do aim to address the issue largley focus on plastics and nutrients but often ignore sediments. This project will explore how international conservation agreements can be better leveraged to reduce all aspects land-based run-off.

Liquid biopsy-based biomarkers for oropharyngeal cancer

Oropharyngeal cancer (OPC) caused by human papillomavirus (HPV) is rapidly increasing globally, with an estimated 173,495 new cases in 2018. Approximately ~10-25% of patients develop recurrences within 2-years. The aim of this NHMRC funded project is to develop a microfluidic chip to permit capture of high-purity and viable circulating tumour cells (CTCs) to early detect recurrences in HPV driven OPC.

Managing environmental risk in Australia's blue economy

Work with an interdisciplinary team to study how aquaculture and windfarming will interact with Australia’s marine ecosystems. Focal areas include marine spatial planning of aquaculture and windfarming and cumulative effects assessments.

Mass spectrometry-based platform to develop novel Proteolysis Targeting Chimeras (PROTACs)

A novel approach called PROteolysis TArgeting Chimera (PROTAC) involves the development of bifunctional hybrid molecules that enable the target protein to be ubiquitinated to promote proteasomal degradation. We aim to develop the first native mass spectrometry-based platform that offers direct characterisation of ternary complex formation, population, stability, binding affinities, cooperativity, or kinetics by PROTACs and overexpressed proteins (both target proteins and ligases) in cells, overcoming the need for purification.

Mechanistic insights into heart disease and inflammatory bowel disease

Well defined risk factors such as high cholesterol, smoking, and high blood pressure worsen the burden of atherosclerosis. Patients with inflammatory bowel disease (IBD) present with a lower prevalence of classic risk factors, however, have at least a 2-fold higher risk of heart disease. Elevated inflammatory cytokines and an altered microbiome are observed in patients with IBD. This project seeks to define the biological link between IBD and heart disease by assessing the role of inflammatory cytokines and bacteria-derived toxins on vascular cells.

Megafauna fossils from the Pliocene of Australia

This project will examine the unique 3.5 million year old megafauna fossils from Chinchilla Rifle Range, Queensland. The project will focus on the taphonomy of the site, and the sequence of fossils collected in systematically excavated sites. Several unusual fossils are awaiting description and taxonomic identification, and palaeoenvironmental proxies revealing ancient Australian habitats can be further interogated.

Micro- and Nanotechnology for the Diagnosis and Treatment of Diseases

This project focuses on multidisciplinary research at the interface between chemistry, nanotechnology, biology and medicine. The primary goal of our research is to advance the diagnosis and treatment of life-threatening diseases such as cardiovascular diseases, cancers and blood disorders with the help of nanotechnology and microfluidics.

Micro/nano-plastic detection and classification using dark-field hyperspectral microscopy

Micro- and nano-plastic debris in aquatic, terrestrial and marine habitats have become significant concern for human health. Due to their tiny size, developing a high-throughput system to detect and classify them is a challenging task. It has been proven that shortwave hyperspectral imaging technology is highly effective in classifying plastics in the size of tens of micrometers. Nevertheless, when the size of plastics reduces to sub-micrometer or nanometers, traditional hyperspectral microscopic system becomes infeasible. This project aims to develop an innovative technology for micro/nano-plastic detection and classification using dark-field hyperspectral microscopy. The scope of the project includes hyperspectral image capture with dark-field microscopy, image processing, and machine learning method development for particle detection and classification. The student will work with a multidisciplinary team in ICT, Environment, Material Science, and Mechanics. 

Microgrid Control and Optimisation for Renewable Energy Integration

Microgrids provide a flexible architecture for deploying distributed energy resources that can meet the wide range needs of different communities from metropolitan cities to rural country areas. This project aims to develop new control and optimisation technologies to implement self-scheduling and self-coordinating among all microgrids in a networked microgrid. It provides a feasible solution for the challenge of both the growing number of microgrids and high penetration level of renewable energy in a power grid. The outcomes of this project will promote the increase in the renewable energy fraction of the total electricity supply in Australia and worldwide.

Microplastic analysis in food and water (tap water & bottles water): Australian dietray intake and potential risks to human health

Microplastics have been widely found in various environments including water, sediment, soil, biota and air. There is growing conern that human can be exposed to microplastics through consumption of microplastic contaminated water and food. This project aim to analyse microplastics in various foods and beverages and assess the human diatary exposure to microplastics and associated health effects

Microplastics pollution of agricultural soils: fate and effects on soil biota and human health

This project aims to investigate microplasic contamination of agricultural soils and their fate and impacts on soil and plants as well as potential toxic effects to human via consumption of microplasic contaminated crops

Modelling and optimisation of hydrogen-based energy systems

The transition to a global energy economy based on renewables is extremely urgent and underway. Hydrogen energy technology has a very imprtant role to play. For instance, it is estimated that the electric power required to produce and export enough hydrogen to satisfy just Japan's needs is more than 1 terrawatt. This compares to Australia's National Electricity Market which peaks at about 30 gigawatts, but also to our readily accessible resource of offshore wind at more than 2 terrawatts.

Modelling bioparticle dynamics in an ion trap.

Trapped ions are a powerful tool for the analysis of charged bioparticles and biomolecules. Paul traps are used for high-resolution, long-duration confinement in this application. However Paul traps have selective stability depending on trap parameters and particle properties. This project would model the impact of permanent and induced electrical dipole moments on the theoretical stability of ion trajectories in a Paul trap and related the limits of electrical confinement of a charge point particle to an electric dipole in an optical tweezers.

Modelling neurodegeneration in the zebrafish and drug discovery

This project aims at establishing new zebrafish models of motoneuron degeneration or neurodegeneration per se. We will use state-of-the-art genome editing tools (optimised CRISPR/Cas9 approach) to manipulate selected genes of interest to both validate their predicted pathogenicity and generate animals developing neurodegeneration. These models will further be used to investigate the underlying degenerative mechanisms and establish drug screening/discovery programs.

Molecular targets program: rapid response to pandemics

The Molecular Targets Program identifies ligands for any cloned and purified protein of therapeutic significance. We have developed native Magnetic Resonance Mass Spectrometry (MRMS) to fast track identification of compounds that can be used for therapy. As viruses contain very few proteins, this platform allows rapid response to viral pandemics. e.g. the discovery of anti-COVID19 anti-virals.

Multiphysics and flexible microfluidic technology

Microfluidics is both the science that studies the behaviour of fluids through microchannels and the technology of manufacturing microminiaturized devices containing chambers and tunnels where fluids flow or are confined. The previous works use rigid materials to construct the devices, and the device's functionality is mainly based on single physics. This project will design and develop a cutting-edge microfluidic technology by exploiting multiple physical coupling and flexible materials to achieve a variety of functions such as micropumps, micromixers, cell sorters, trappers etc. This technology will be applied in the lab-on-a-chip system for disease diagnosis, prognosis and treatment.

Nanobubble technology: fundamentals and applications in biomedical and chemical engineering

Nanobubble technologies have applications in wastewater treatment, surface cleaning, sanitization, and therapeutics towards some age-associated diseases. This project focus on fundamentals of nanobubbles by exploring the stability, nucleation and dynamics of nanobubbles. The project aims to develop technologies to generate nanobubbles and apply the gained nanobubble technologies in agriculture to treat biomass, in poultry and dairy to achieve fast promotion of animal growth, in aquaculture to increase productivity and feed-conversion ratio with short harvesting cycle, in catalyst chemistry for renewable energy, and in therapeutics to treat diseases

Nanomachineries for Exosomes and Exosomal Biomarkers Analysis

Exosomes are nanoscale (≈30–150 nm) extracellular vesicles of endocytic origin that are shed by most types of cells and circulate in bodily fluids. Exosomes carry a specific composition of proteins, lipids, RNA, and DNA and can work as cargo to transfer this information to recipient cells. Recent studies on exosomes have shown that they play an important role in various biological processes, such as intercellular signaling, coagulation, inflammation, and cellular homeostasis. These functional roles are attributed to their ability to transfer RNA, proteins, enzymes, and lipids, thereby affecting the physiological and pathological conditions in various diseases, including cancer and neurodegenerative, infectious, and autoimmune diseases (e.g., cancer initiation, progression, and metastasis). Due to their unique composition, easy accessibility and capability of representing their parental cells, exosomes, and exosome containing RNAs, proteins draw much attention as promising biomarkers for screening, diagnosis and prognosis of these diseases via non-invasive or minimally invasive procedures. Therefore, isolation and analysis of tumor-derived exosomes and exosome containing biomarkers at the early stage of the diseases could significantly improve the capacity to diagnose the diseases thereby improving outcomes. The Shiddiky group is pursuing studies of the development of multifunctional magnetic nanomaterials based technologies and devices for the highly selective isolation and sensitive detection of exosome and exosomal biomarkers (mRNA, proteins) in patients with cancer and other disease.

Nanoparticle impregnated carbon as a reductive catalysis for environmental toxin removal

Nanoparticles have a great potential to be used in water treatment due to its high surface area. This can be utilised efficiently for removing toxic metal ions, microbes and organic matter from water. However, due to their sizes, nanoparticles often form aggregates/agglomerates lowering their activities. To prevent these, further processing including surface passivation is applied. The use of activated carbon into the nanoparticle systems is another strategy as it is simple and economical. Activated carbon was recorded to be used for a multitude of applications, including water filtration/treatment, gas phase adsorption and decolourising agents in the food industry. Research into improving both structure and applications has grown exponentially in recent decades as environmental sustainability has become a key focus, especially the areas involved in environmental remediation. Combination of the nanoparticle and activated carbon provides an excellent platform for the environmental applications such as enhanced capacities and rates.

Natural products drug discovery: New method to identify disease-associated drug targets

Target identification is crucial for rational drug design and is a current bottleneck for advancing bioactive compounds through the discovery pipeline. This project will use the power of native mass spectrometry to establish and validate a disruptive new platform to elucidate targets of bioactive compounds by direct detection of protein-small molecule binding. The proposed approach will accelerate current target identification as it will not need to modify bioactive compounds or proteins to achieve this outcome.

Neural Automated Reasoning

Complex formal proofs require significant effort and tools such as interactive theorem provers, whereas automated reasoners are often limited by the size of the problem or computational time. Recent advancements in machine learning have led to new tools, such as Sledgehammer, that use traditional theorem provers in smart ways to improve run time and automation. New algorithms such as HyperTree Proof Search applies ideas from Monte-Carlo Tree Search and deep reinforcement learning to push the boundaries of state-of-the-art theorem proving. This project aims to adopt the above ideas to develop new ML-based tools for Isabelle/HOL that can reason about logical formulae faster and more automatically.

New intelligent and human-centred approaches to supporting airline pilots’ decision making in complex situations to improve flight safety

One of the greatest challenges to airline flight safety, is how well the pilots diagnose and respond to complex abnormal or emergency situations, such as multiple failures, false alarms, and inoperative systems, which can arise during flight of modern aircraft. Aircraft have evolved to become technically highly sophisticated, becoming more ‘robot than machine’, but approaches to pilot support in the modern cockpit lag well behind. Pilot support in the cockpit remains limited to traditional approaches (the checklists and procedures of the Quick Reference Handbook), effectively leaving pilots unsupported, 'mostly on their own’ to deal with complex, safety critical situations. The aim of the research is to harness areas such as AI/reasoning/machine learning alongside research in Human Computer Interaction to develop new approaches to supporting pilot decision making, thereby enabling pilots to diagnose and respond more safely and effectively to the complex abnormal or emergency situations that they will encounter when flying modern aircraft.

New methods in stone tool functional analysis

This project will seek to develop new approaches for determining stone tool function. Emphasis will be placed on experimental and quatitative methodologies, with application to key questions about early human adaptation to new and changing environments.

Next Generation Biometric Technology: Privacy-Preserving 3D Fingerprint Biometrics

The use of 2D fingerprint technology has become widespread in various authentication applications, such as mobile phones, laptops, and building access control. However, this technology has limitations, as it cannot fully replicate a real finger and is vulnerable to spoofing attacks. As a result, there has been a shift towards the development of 3D fingerprint biometrics, which offers benefits such as hygienic, contactless, anti-spoof, and natural representation. The aim of this project is to explore 3D fingerprint biometrics and to develop an AI-guided 3D fingerprint biometric system. The project will involve the application of AI/deep learning on 3D point cloud data. Feel free to contact me for further discussion.

Novel agents for anti-fungal drug development

The opportunistic human pathogenic fungus Aspergillus fumigatus causes severe systemic infections including Invasive Aspergillosis (IA), a major cause of life-threatening fungal infections in immuno-compromised patients. An over-whelming number of reports appeared in 2020 demonstrating that COVID-19-associated pulmonary Aspergillosis (CAPA) is one of the leading factors affecting morbidity in critically ill COVID-19 patients with some reports even classifying Aspergillosis as a significantly under-recognized ‘Superinfection’ in COVID-19. Drug resistance among fungal pathogens is continuing to develop into an increasingly serious threat to public health and health-care systems worldwide. This PhD projects entails the development of novel antifungal therapies that are urgently needed using our established and unique combined in-silico/SPR drug discovery pipeline evaluating a number of new protein targets.

Numerical solution and simulation of electron transport and plasma models in various application areas

This project centres around construction of simulation frameworks for a variety of high impact plasma and electron transport applications, such as atmospheric lightning discharges, low temperature plasma-solid interactions through to magnetically confined fusion plasmas. Areas of investigation can be tailored to candidate expertise & interests, including numerical solution techniques for transport equations, the closure problem, machine learning and AI in computational science, kinetic or Monte Carlo methods.

On-Farm' Devices and Technologies for Broad-Scale Disease Surveillance of Crop Plants

Plant pathogens reduce global crop productivity by up to 40% per annum, causing enormous economic loss and potential environmental effects from chemical management practices. Thus, early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control and management are crucial. Detecting and quantifying pathogen species and their relevant genetic biomarkers in plant extracts at the early stages of the diseases is notoriously difficult to access via conventional methodologies. This is mainly because they are either too slow to enable efficient intervention and application of fungicides (visual observation of symptoms in the field) or are too expensive and technically complex to be used by non-specialized technicians on an industrial scale. The development of an affordable, sensitive, specific, user-friendly, rapid and equipment-free method for broad-scale disease surveillance in crop plants, based on “on-farm” pathogen detection and quantification, is of great interest to the agricultural industry and plant biology. The Shiddiky Laboratory focus to develop portable devices and technologies for ‘on-farm’ analysis of pathogen species and pathogenic biomarkers in unprocessed plant extracts. Such a device would allow more rapid and cost-effective detection, control and management of the plant diseases.

Optimised Artificial Neural Network-based Cyber Intrusion Detection Systems

As the number of advanced cyber attacks is rapidly increasing in the modern world, it is crucial to detect attacks as soon as possible to prevent them from reaching their final goal and causing destructive damage. Therefore, a robust cybersecurity system is needed to detect and respond to potential cyber-attacks in a timely manner. Although automated intrusion detection using artificial intelligence has been proposed by many researchers, the performance of these methods still needs improvement. This project aims to review the applications of optimisation algorithms, such as the whale optimization algorithm (WOA), in improving the performance of networks (ANN)-based solution to detect cyber-attacks. Different optimisation algorithms will be analysed and compared to find the method that can outperform other methods.

Oral microbiome as a biomarker to early detect heart failure

Heart failure is a major global pandemic affecting more than 38 million people worldwide. It has been suggested that poor oral hygiene and periodontal diseases are related to a higher risk of developing cardiovascular disease. However, the underlying cause of this phenomenon has not yet been investigated. We are aiming to profile the oral microbiome content in patients with heart failure

Orchestration of Hybrid Agents and Autonomous Swarms

Traditional control techniques have limitations when it comes to ensuring that a swarm of autonomous agents (whether fully or partially automated) fulfill their tasks, while at the same time observing the rules of engagement. The project will explore the possibilities of command rather than control over such multi-agent systems. (The project suits someone who is eligible to work for Australian Defence).

Outlier detection in data streams

Data streams are sequences of data that are continuously transmitted to a receiver. Outlier detection is to identify abnormal data, or data that are significantly different from normal data. The problem of detecting outliers from data streams has important applications and has attracted a lot of attention from researchers. However, there are still many challenges in accurately and efficiently identifying outliers, that is, how to effectively distinguish normal data from outliers, and how to achieve real-time identification. This PhD project aims to develop novel techniques for the problem.

Photonic-Atomic based Quantum Devices

We are developing microfabricated silicon nitride based photonic waveguides to interface with rubidium atoms as a platform for realising quantum devices. The first device in this project aims to demonstrate a wavelength converter from the 780 nm light used in atomic magnetometry to the long-distance telecom compatible 1529 nm light. This is an experimental physics project which includes fibre optics, photonics design work, microfabrication, atomic physics, and vacuum systems with the goal of advancing towards manufacturable devices.

Planning for aquaculture expansion under climate change

Aquaculture is one of the fastest growing food sectors in the world, with great potential for expansion. Climate change poses a significant threat to aquaculture production - from potential losses in infrastructure to sub-optimal growth and production rates, but climate change is rarely included in aquaculture development plans. In this project you will work with an interdisciplinary team to assess and incorporate climate risk into aquaculture planning to future proof aquaculture production under a changing climate.

Plasma modelling and simulation for magnetically confined fusion plasmas

A variety of projects are available in different modelling areas, with the focus applied to modelling a variety of important physics scenarios important to tokamak plasmas, such as those anticipated in ITER. Equilibrium plasma discharge, tokamak disruption, runaway electrons, edge-plasma, and surface wall interaction applications are examples of focus applications.

Point-of-care (POC) diagnostics

The development of an affordable, sensitive, specific, user-friendly, rapid and equipment-free diagnostic method that can detect diseases at the time and place of patient care (i.e., point-of-care) using minimal specialised infrastructure, has the potential to transform health care to many millions people both in the developed and developing countries. Recent advances in sequencing and proteomics technologies have now given rise to a large number of potentially useful genetic, epigenetic and other novel molecular biomarkers for the development of diagnostic methods for many diseases including cancer, infectious and neurodegenerative diseases. Despite these great input from biomedical engineering, significant technical challenges for achieving a functional POC device are yet to be overcome. This is mainly due to the lack of sensitive, specific, rapid and low-cost readout methods. The Shiddiky group is pursuing studies of improving existing and developing entirely new methods that can rapidly detect cancer, infectious and neurodegenerative diseases.

Portable Instrument for Quantification and Genotyping of Waterborne Pathogens

Despite the tremendous efforts in developing effective on-site biosecurity and best management practices, waterborne parasites still cause significant health and economic burden worldwide. Early and rapid diagnosis together with an understanding of disease severity is critical for preventing parasite spread and enabling effective management strategies. Current routine diagnostic tests for waterborne parasites are not suitable for on-site detection. Shiddiky Laboratory is working on developing novel biosensing platform devices for the quantification and genotyping of waterborne parasites in surface and recreational waters. The device can be used to ensure improved waterborne parasite management, risk prediction, and rapid mitigation of impending outbreaks.

Precision-engineering of core-shell structures for prevention and treatment of diseases

This project harnesses the biosynthesis capacity of microbial cells to produce polymeric self-assemblies that can be engineered to incorporate protein functions such as antigen, binding domains and enzymes. This approach uses metabolic engineering and protein engineering to exploit the vast biomaterials design space for generation of innovative smart materials that form core-shell structures and exhibit advantageous properties toward such as uses as antigen carrier in vaccine applications or for targeted delivery of active compounds.

Precision-engineering of core-shell structures for prevention and treatment of diseases

The spread of cancer (metastasis) accounts for 90% of cancer deaths. Critically, this belligerent disease is highly resistant to conventional therapies, and new molecular targets and therapeutic avenues are urgently needed. Prof Richardson discovered innovative anti-cancer drugs that can increase the expression of a metastasis suppressor protein, NDRG1, that prevents tumour cell spread (Fig. 1). He also discovered these same drugs overcome resistance of cancers to chemotherapies by overcoming the drug efflux pump, P-glycoprotein. This project will involve examining the functions of NDRG1 and its targeting by our novel drugs to elucidate the molecular mechanisms involved in their anti-tumour activity. A range of state-of-the-art techniques will be used to maximise student training, including: tissue culture, western blot analysis, immunohistochemistry, medicinal chemistry, and confocal microscopy.

Privacy Attacks and Defences in Deep Neural Networks

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their privacy leakage. Notably, instances such as ChatGPT inadvertently revealing its training data through deliberately designed prompts underscore the pressing need to address privacy vulnerabilities in DNNs. This proposal seeks to delve into the vulnerabilities of DNNs to privacy attacks, examining potential threats stemming from learning paradigms, model architectures, training data, training processes and inference outputs. By understanding these risks, the project aims to develop robust privacy-preserving mechanisms and effective defences against privacy leakage, ensuring that the deployment of DNNs aligns with stringent privacy standards.

Privacy-Aware and Personalised Explanation Overlays for Recommender Systems

AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.

Process-driven sediment regime changes in semi-enclosed tidal estuaries

The Broadwater on the Gold Coast is a large semi-enclosed tidal estuary that forms the southern portion of Moreton Bay. While the estuary receives fluvial sediments from four major river catchments, the dynamic coastal processes have long dominated sediment inflow into the estuary. The study seeks to quantify the ratio of catchment-derived to marine-derived sediments in the estuary, and determine refined proactive management mechanisms for maintaining ecosystem function and navigability within the estuary.

Protein engineering of protein switches for development of diagnostics tools

This project combines advanced protein engineering with materials science and biotechnology. Sensitive and specific detection of serum antibodies is often used to diagnose infections. This project aims to develop a simple qualitative/quantitative device for detection of antibodies of interest. It will involve protein engineering of protein switches to incorporate antigens while attached to biomolecular scaffolds. Binding of the antibodies to the antigens will activate the protein switch which will result in release of a signal.

Quantifying Australian aquaculture's environmental footprint

Aquaculture is a fast growing industry in Australia - which is known for production safe and relatively sustainable seafood products. Australia has great potential for aquaculture expansion but currently has limited knowledge of how aquaculture impacts the environment now and in the future. This project will work to quantify major environmental impacts from Australian aquaculture (nutrient pollution, GHG emissions, etc.) and potential impacts on habitat, species and ecosystem services. The work is essential for sustainable Australian aquaculture and supports many of Australia's Blue Economy initiatives.

Quantum causal models

The 2022 Nobel prize in Physics was awarded to the experimental demonstration of quantum entanglement and its counterintuitive properties, in particular the violation of Bell inequalities. A modern way to understand this phenomenon is as a failure of a classical causal model that satisfies relativistic constraints on causal structure. The program of quantum causal models aims at resolving the puzzle of Bell's theorem by extending the classical framework of causality to a quantum setting, while maintaining compatibility with relativistic causal structure. This project will involve further developing the framework of quantum causal models and addressing various open questions, such as counterfactual reasoning, indefinite causal structure, and/or potential applications to quantum information processing tasks.

Quantum Foundations and Quantum Causality

Quantum technologies are poised to become major drivers of scientific and economic growth in the 21st century. On the other hand, quantum advantage over classical computers has only been demonstrated for a few classes of algorithms. This interdisciplinary project will tackle the key question for unlocking the benefits of quantum information processing: what gives quantum mechanics its information-processing power beyond classical physics? It will explore the hypothesis that quantum advantage is associated to fundamentally different ways in which causality operates in the quantum and classical regimes.

Quantum machine learning for efficient quantum tracking

The project will apply quantum machine learning to the problem of tracking the state of an open quantum system. Specifically, we want to find the most memory-efficient classical apparatus, which performs adaptive quantum measurements so as to maintain the state of the quantum system in a stochastically varying conditional pure state. While this problem can be attacked by exact methods in classical numerics, these are very computationally expensive, so machine learning is an obvious alternative. Most interestingly is to use genuine quantum machine learning. That is, to perform quantum machine learning experimentally, where the system itself is part of the machine learning loop. This project thus has an experimental quantum photonics supervisor also.

Quantum observers

Despite its enormous scientific and technological success, quantum theory suffers from deeply puzzling conceptual problems, none more vexing than the quantum measurement problem. It involves inconsistencies that arise when considering the treatment of "observers" as physical systems amenable to a quantum description. Recent results on extended versions of the "Wigner's friend paradox" exemplify the measurement problem in the form of rigorous no-go theorems, such as the "Local Friendliness" no-go theorem. It shows that certain sets of a priori plausible assumptions cannot be simultaneously satisfied by any theory that can accommodate certain phenomena where an "observer" can be treated as ordinary systems subject to quantum-mechanical operations. This project, which has both a conceptual and a technical component, aims to propose increasingly convincing experimental realisations of such phenomena, by asking what are sufficient conditions for a system to be deemed an observer, and what experimentally feasible but increasingly sophisticated quantum systems may provide models of quantum-coherent observers.

Quantum Program Verification

Software testing can only show the presence of bugs but never their absence, so it is crucial to mathematically prove the correctness of programs in mission-critical domains such as aerospace, defence, finance, health, etc. This practice is called formal (program) verification. The advancements in quantum computing extend the application of formal verification to quantum programs, which is uncharted territory. This project will develop new verification techniques that are suitable for quantum programs, possibly using quantum computing algorithms.

Real-time monitoring of the state of composite structures using machine learning techniques for predicting remaining useful life of aerospace parts

The problem of real-time monitoring of the state of composite structures (such as for example those found in airplanes) requires signal processing and machine learning on the one hand, but extended with logical reasoning that creates explainable decision support.

Recommender Systems based on Social Networks

This project focuses on various methods that are used for the recommender systems based on social networks. Students will explore research issues in recommendation algorithms, and gain experience in applying appropriate methods to predict user preferences in different settings. It is to form the in-depth analysis of data-driven behaviors strongly interdependent with each other. Students will need to propose recommendation solutions for social network users and evaluate the prediction accuracy after applying the methods. Students will explore research issues to design the underlying models and algorithms for those heterogeneous and interdependent behavioral data to make predictions and recommendations, as well as develop software prototypes.

Reducing human impacts in marine protected areas

Marine protected areas are the main conservation tool used to address the biodiversity crisis in our oceans. They are also a major focus of international conservation agreements such as the recently adopted Kunming-Montreal Global Biodiversity Framework. This project will use novel methods to quantify human impacts in marine protected areas through time and develop strategies and recommendations to reduce these impacts and improve the effectiveness of marine protected areas.

Reducing wastewater impacts on nature and people

Nearly 1/3 of coral reefs are threatened by poor qater quality and there are an estimated 800,000 human deaths each year due to sanitaiton-related water pollution. Improved sanitation has the potential to achieve benefits for both nature and people - but is often poorly understood (particularly in communities with little access to resources). This project will asess opportunities for reducing nutrient pollution to achieve both ecosystem and human health objectives - with the potential to incorporate risk and uncertainty from climate impacts.

Research on Translations Technologies for Diagnosing, Monitoring and Treating Diseases

This research focuses on multidisciplinary research at the interface between chemistry, nanotechnology, biology and medicine. Research at this interface has the potential to generate breakthroughs in fundamental science as well as lead to advanced technologies for diagnosing, monitoring and treating disease. Current (selective) research projects are the following: Point-of-care (POC) diagnostics; microfluidic methods for the detection of cancer; portable devices for cancer epigenetics; nanomachines for exosome and exosomal biomarkers detection; and superparamagnetic materials in biosensing applications.

Safety Management Systems Differently: developing tools for safety practitioners of the new era

Safety management systems are a reality and a requirement in many industries, from aviation and healthcare, to oil and gas and constructions. Also known as Occupational Health and Safety (OHS) System, Health, Safety and Enviroment (HSE) Systems, these systems have not been able to improve the safety records as expected and the limitation pointed out by many scholars is the reliance on outdated assumptions and limited evidence. Considering new approaches to safety management, such as safety-II, safety differently, resilience engineering, and other, this research project aims to analyse the limitations of safety management practices commonly employed by safety management sytsems and update or develop new practices. The ultimate goal is to help industry make their SMS more effective.

Saliva test to triage lung cancer nodules found on CT scans - a pilot study

About 15% of lung cancer patients survive beyond 5-years. CT screening to early detect lung nodules has been investigated, however false positive results, unnecessary radiation exposure are some of the drawbacks. We propose an innovative approach to identify nodules found on CT scans using breath analysis and liquid biopsies. This new multidisciplinary partnership will lay the foundation for future collaborations.

Security of Industrial Internet of Things in Industry 5.0

With the emergence of the Internet of Things (IoT) and Industry 4.0, there is a trend for applying these services and applications in a large-scale industrial area. The IoT paradigm has changed the way of interactions with the things that surround us. In essence, the IoT promises ubiquitous connection to the Internet, turning common objects into connected devices. This project will review the different architectures of IIoT, and systematically study the security challenges associated with interoperability, access control, privacy, and trust-related issues, in general. The project will also identify the gaps in the state-of-the-art security techniques and requirements to determine the security services in the IIoT environment and propose potential mitigation techniques to address these gaps.

Segmentation of powerline corridors in complex environments

Conductors in powerline corridors are thin, so only a small number of laser points get reflected, which makes it difficult to effectively extract the conductors using the aerial point cloud data. We have recent success in extraction of the conductors even when conductors are in bundles of 2 or 4 sub-conductors. However, power line corridors often exist in complex environments (e.g., forests), where occlusions, missing data and noise are regular phenomena. The advancement of recent deep learning techniques will be useful for high-performance powerline corridor segmentation in complex environments.

Simulation of battery electrolytes using artificial intelligence

This project combines artificial intelligence with molecular simulation to develop a predictive understanding of electrolyte solutions for developing the next generation of electrolytes for improved battery technology. Electrolyte solutions are one of the most important substances on earth, playing a central role in energy storage, carbon capture and conversion and essentially all of biology. Until recently, however, we have been unable to predict even their most basic properties. Recent advances in the field of AI and molecular simulation mean that for the first time it is possible to accurately predict their properties with existing software. This project will train students in the exciting and rapidly growing area of artificial intelligence for materials science. Many companies including Microsoft, DeepMind, Google Research and Schrodinger are currently investing in this area.

Sports Analytics

We have recently developed state-of-the-art techniques for sports video processing using deep learning and strategy and match outcome anlaysis using probabilistic reasoning. This project aims to extend these results to deal with different sports, such as soccer, baseball, basketball, etc. We are also interested in developing applications for match analysis, visualisation, match outcome simulation and so on. The current methods may be combined with large language models to provide smart responses to user queries.

Stimuli-responsive nano-therapy for infectious disease

Antimicrobial resistance to commonly prescribed antibiotics remains an ongoing global threat. This project will develop theranostic nanomaterials that overcomes antimicrobial resistance and allows both diagnosis and stimuli-responsive treatment of infectious diseases in one dose. The outcome of the project will open a whole new way to manage and treat infectious diseases.

Stone Age archaeology of southern Africa

This project will investigate the archaeology of southern Africa to better understand the origins and evolution of Homo sapiens. The focus will be on the Late Pleistocene record in regions that have been less well-studied (i.e., the deep interior savannah and desert environments).

Streaming Data Privacy Protection, Secure Analysis and Mining

With the exponential growth of streaming data from various sources in both volume and content, privacy protection for streaming data and their secure analysis are becoming increasingly important. Considering the properties of streaming data including mass volume (unbounded size), heterogeneity, dynamicity, concept drift and feature evolution this project applies multi-fold theories and techniques including secure computing, privacy protection, machine learning, intelligent searching, data mining in an effectively coordinated way. The project first studies how to discover and measure sensitive information of data instances, including features and labels, in data streams. It then investigates suitable models, schemes and mechanisms for effective protection of the sensitive information while preserving the required data utility. Finally it develops new techniques and methods for various privacy-preserving streaming data analysis and mining, including statistical analysis, association mining, classification and clustering, and evaluation of their performance.

Studies on Micro- and Nanoscale Systems

Micro- and nanoscale systems exhibit unique properties that can’t be predicted from the theory of large-scale systems. In order to develop new strategic micro- and nanotechnologies, open questions on the behaviour of very small systems need to be addressed. Micro- and nanoscale systems exhibit unique properties that can’t be predicted from the theory of large-scale systems.

Systems biology in environmental research, food analysis, food security, and livestock science

In collaboration with colleagues at QAAFI and other institutions we are using NMR-based metabolomics as analytical platform technology to characterise the composition of foods such as honey and native Australian fruits. This involves characterising the potential of native Australian fruits as commercial food sources and developing methods for the detection of food fraud especially in honey.

Taking forensic imaging into the ultraviolet and infrared with chemical dyes

Using full-spectrum photographic equipment available at Griffith University, this synthetic project will develop new chemical dyes, stains, and fluorophores, that primarily emit in the UV and IR regions of the electromagnetic spectrum. These emissive treatments will then be trialled and validated against existing forensic treatments.

Targeting thrombosis as an innovative way to treat cancer

Cancer is a major cause of illness in Australia and has a substantial social and economic impact on individuals, families and the community. Although technologies continue to evolve, currently, most cancers could not be completely cured. This project will explore different innovative ways involving thrombosis for more effective treatment of cancers

The fusion of reinforcement learning and automated reasoning

This project explores the relationship between reinforcement learning (RL) and probabilistic model checking (PMC), as both are built upon the underlying model of Markov decision processes. On the one hand, PMC may be used to guide and constrain an RL agent when exploring optimal solutions so that the agent operates within a "safe region". On the other hand, RL may be used to improve the performance of model checking algorithms through statistical methods. We aim to improve the state-of-the-art of both worlds.

The rational use of simulators and training devices for training in transport industries

Simulators and training devices are applied in a range of educational settings. From vocational and tertiary degree to high risk industries, these educational technologies are engaging, they place students at the centre of the learning process, force students to be active and serve as a great risk-free enviroment for safety critical training. Despite being extensively used for trianing operators in aviation, maritime and rail, there is still a perception that high fidelity simulators are always a preferred technology. The assumtion is the similar to a real enviroment, the better. However, recent research has shown that simulators and training devices will never fully reproduce reality and the low fidelity ones are as helpful if employed with appropriate pedagogy and support materials. In this research project, the objective is to assess how different simulator and training device tehnologies can employed to enhance training. Which tasks and skills can be developed accross a range of devices taking into consideration learning objectives, training outcome, quality, length and costs?

The role of government in sustainable development and addressing climate change

Governments have a key role to play in achieving sustainable development and addressing climate change. The object of this research is to synthesise policies, plans and strategies that will assist with this transition. While commitments have been made at the international level, and some organisations have made improvements at the local level, there is a strategic gap between the two that has not been fully researched. PhDs can be on either sustainability or climate change and take a theoretical or applied approach. The methods used include case studies, comparative analysis, policy analysis, stakeholder interviews, or surveys.

The use of minimally invasive biomarkers in the management of glioblastoma patients

Glioblastoma (GBM) is the most frequent and aggressive form of brain cancer in adults. Currently, there are no biomarkers to reliably evaluate disease progression during treatment, leading to delays in important clinical interventions. To improve noninvasive monitoring of cancer and find new potential targets for therapies, liquid biopsy approaches, including the use of extracellular vesicles (EVs), circulating tumour cells (CTCs) and circulating tumour DNA are being investigated. The liquid biopsy approach has advantages over tumour tissue biopsy since it allows serial timepoints collections and in a minimally invasive way. We aim to expand results obtained on EVs, ctDNA and CTCs isolated from blood and saliva of GBM patients, validating them in larger cohorts and identifying novel biomarkers to help in the diagnosis and prognosis of this disease.

Theory of photonic quantum state generation and its use for cybersecurity

In networks with distant parties, light provides an excellent way to transmit quantum information because of its fast propagation and low decoherence. However, these advantages are accompanied by a drawback - the lack of appreciable interactions between photons at the single-photon level, which makes it more challenging to create entangled multi-qubit states with photons compared to other carriers of quantum information. The objective of this theoretical project is to develop optimal state preparation procedures and incorporate them into strategies for showcasing novel quantum cybersecurity protocols.

Touch screen technology interaction in the flight deck

Flight decks are constantly evolving. New technology is constantly implemented in it to enhance safety. However, with such new additions there are also some challenges that might arise. These need to be understood to maintain safety. Touch screen technology is one of the newest additions in the flight deck. In this project, the interaction with such technology during various flying conditions will be explored. Any issues pilots might face when interacting with such technology will be understood. Training required to know when to use and when not to use touchscreen technologies will be examined too. As seen with many other types of flight deck technologies; a new piece of technology that starts off in the commercial airline industry might trickle down into the general aviation industry also. Hence, suitable recommendations will be made for the wider aviation industry.

Traditional Chinese medicine (TCM) and brain function

Many TCMs have a neuroprotective effect; that is, they protect the central nervous system against damage or degeneration due to diseases such as Parkinson’s disease. Working with TCMs with a known neuroprotective effect, we can isolate and identify the major constituents of selected TCM and test the compounds against cell-based models of Parkinson’s disease. By analysing and testing TCMs, we can determine their mechanism of action and develop new ways to treat neurological diseases.

Trustworthy Graph Neural Networks

Graph machine learning, graph neural networks, in particular, is the frontier of deep learning. There has been an exponential growth of research on graph neural networks (GNNs) in the last few years, mainly focusing on how to develop accurate GNN models. The trustworthiness of GNNs is less considered. In this project, we will explore how to develop trustworthy GNN models. The key aspects, including robustness, explainability, fairness, and privacy, will be taken into consideration when developing GNN models.

Two-dimensional nanoporous structured high performance gas evolution electrocatalysts

This project aims to develop nano-catalysts with high catalytic activity and rapid gas detachment properties for efficient fuel gas production. Heterogeneous electrocatalytic gas evolution reactions are important for clean energy generation and storage technologies, but high overpotentials caused by slow gaseous products’ detachment from catalyst surface severely hinder their efficiencies. Expected outcomes include insights into gas bubble formation and evolution during electrocatalysis, effective catalyst structures to mitigate negative effects of gas bubble formation, and improved catalytic efficiency of gas evolution reactions and develop high performance electrocatalysts for fuel gas production.

Ultra-high-quality generation, communication, and processing of photonic quantum information

Photons are low-noise and flexible quantum systems, perfect for quantum communication and quantum information processing. However, to date, it has not been possible to create key photonic quantum states such as high-fidelity states of many correlated photons and complex heralded entangled photon states. These projects will use high-efficiency photon-pair sources developed at Griffith University and world-leading superconducting photon detectors to develop and generate these important photonic quantum states. 

Understanding and Resisting Adversarial Attacks in Deep Neural Networks

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their security. One particularly insidious threat is the emergence of adversarial attacks, wherein malicious actors deliberately manipulate input data to deceive deployed DNNs, leading to misclassifications or compromised performance. These attacks pose a significant challenge, demanding a comprehensive understanding of their mechanisms and the development of robust defences. This proposal aims to delve into the intricacies of adversarial attacks, exploring their impact on DNNs and proposing effective strategies to fortify these models against such sophisticated threats.

Untargeted metabolomics and lipidomics for monitoring environmental health

Advancements in analytical capabilities make it possible to simultaneously measure a comprehensive suite of physiologically important biomolecules in living organisms. These molecules can provide a ‘snapshot’ of the health and general well-being of an organism. This research project aims to establish robust methodologies to make molecular monitoring a reality. The PhD project will apply untargeted metabolomics and lipidomics analysis to evaluate and compare the status of aquatic species from pristine and human-impacted locations, with the goal of establishing biomolecular signatures as indicators of environmental health.

Unveiling Backdoor Threats in Deep Neural Networks

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their security. One particularly insidious threat is the emergence of backdoor attacks, which involve stealthily implanting malicious features or patterns during model training, enabling unauthorised access or manipulation of the neural network's behaviour under specific conditions, compromising its integrity and functionality. Notably, recent reports have raised suspicions about a significant number of pre-trained DNN models from Model Zoo being vulnerable to backdoor attacks. This project investigates backdoor attacks on DNNs, aiming to expose attack mechanisms, assess real-world implications, and propose detection/mitigation strategies for robust AI systems. The exploration includes defining and elucidating backdoor attacks, examining implantation techniques, showcasing instances, and consequences, and analysing recent cases for lessons.

Unveiling the normal and pathogenic role of neurexins in the developing brain

Neurexins are a family of genes that have been associated with several neurological diseases. We have generated a series of innovative zebrafish CRISPR-mutants that should allow to better understand the role of these genes in the developing brain. We will combine single-cell transcriptomics studies, high-end imaging, and behavioural approaches to highlight their critical function in brain development and plasticity.

Use of natural product scaffolds in the generation of novel chemical libraries for drug discovery

Natural products display chemical complexity and diversity and they inherently interact with biomolecules (e.g. proteins, DNA), making them an ideal source of unique scaffolds for screening library synthesis. This medicinal chemistry project will generate unique biodiscovery libraries that will be fully characterised using spectroscopic methods before being screened in anti-infective, anti-cancer, or ion channel functional assays.

Using AI for learning and teaching

This project aims to research into the application of AI to assist with learning and teaching (L&T). There are many aspects of L&T that can benefit from the use of AI. However, instead of having AI playing a central role, e.g. run a class, this research focuses on the use of AI in a supporting role such as providing feedback to students, assist teachers in marking students work, or even directly mark student’s formative work etc. This applied research designs and develops AI tools to assist in specific L&T activities, applies these tools and evaluate the results. It explores the scenarios where AI tools are of benefit and determines how the tools should best be utilised.

Using host guest chemistry to make forensic science greener

This project will explore hosting three of the most ubiquitous chemical developers for fingerprints within cavitands so as to modify their solubility. The aim will be to make those developers water-soluble, thereby allowing the elimination of costly and environmentally damaging solvents from common forensic treatments.

Virus-like particle cell entry mimics for antiviral drug discovery

Virus-like particles are non-infectious mimics of viruses that can often enter cells via the same receptor-mediated pathways as the viruses they resemble. Our work in this area includes the development of fluorescent analogues of important human pathogens and the creation of particles of different shape and size to understand the fundamentals of virus-cell interactions.

Whale Economy – the value of whale watching to the Australian blue economy

Globally, the whale watching industry has been increasing in size and economic value since the 1990s, yet, little is known about the importance of this sector to the local economy. This research project aims to update and establish the latest figures on this sector for Australia recognising the increase of whale watching and swim with whales in Australia. The most recent estimates on the contribution of whale watching to the economy date back to 2008, where it was found that over 1.6 million people went whale watching, generating AUD $47 million in ticket expenditure and AUD $264 million in total tourism expenditure. This project will involve the analysis of historic customer number and revenue data collected by whale watch operators and may also involve collecting data directly from whale watch participants via an expenditure survey.

When Glyco meets Nano: Developing novel tools to explore complex glyco-interactions

Micro-technologies in the form of Micro-Electro-Mechanical Systems (MEMS) and micro-plasmonics platforms offer the potential for high-resolution, high-throughput label-free sensing of biological and chemical analytes. Silicon carbide (SiC) is an ideal material for augmenting both MEMS and plasmonics routes, however such inorganic surfaces need to appropriately and efficiently functionalised to allow subsequent immobilisation of functional biomolecules. To this end we trialled various organosilane-based self-assembled monolayers for the covalent functionalisation of 2-dimensional SiC films, and have now developed an affordable, facile one-step method. Using high-throughput glycan arrays as our model system a novel platform that has the potential to combine established array technology with the label-free capabilities of MEMS or plasmonic systems is one step closer. Using a similar functionalisation route, we have extended the use of organosilanes to biofunctionalise the surface of 3-dimensional nanoparticles, specifically carbon dots. Carbon dots are cheap, biocompatible, chemically stable, heavy-metal free quantum dots, of low toxicity that offer an alternative approach for bio-imaging and -sensing applications. Again, employing glycans as our model system, we are now using our biofunctionalisation approach to generate glycan-coated carbon dots that we are using to explore complex glyco-interactions.

Who’s in the driving seat? – systems biology characterisation of insect-symbiont interactions

The metabolic interactions between the endosymbiont Wolbachia and its insect hosts depend on the combination of Wolbachia strain and host organism and range from mutualistic symbiosis to parasitic interactions. With a combination of metabolomics and physiological techniques we want to characterise these interactions and the role they play in hindering the transmission of insect-borne virus diseases.

Zebrafishing for novel bioactive molecules

Whilst many current drugs are derived from nature, many more bioactive molecules have still to be discovered. To help speed up discovery, we will develop i) a unique multipurpose zebrafish model combining different transgenic fluorescent markers/sensors and ii) automated assays to screen existing diverse chemical libraries for bioactive molecules. Validated assays will then be used to screen natural product libraries and start looking for the drugs of tomorrow.

The Daley Family

Top up scholarships supporting candidates to undertake a PhD in medical research, awarded in honour of Mr Thomas Daley

Large Language Models: Continual Learning under Fairness Constraints

Despite the recent advancements in Generative AI and Large Language Models in particular, these models still struggle to provide fair outcomes across societal groups. At the same time, models need to continually learn to adapt to our ever-changing world. This project looks at developing models that can mitigate bias in the face of dynamic updates to its pre-trained and fine-tuned knowledge base.

AI classification of algal microscopic images

Prompt identification and classification of algae and cyanobacteria species are paramount for water utilities and city councils to minimize negative ecosystem and public health effects. While real-time sensors can provide an estimation of total biovolume, traditional light microscopy is still the most common method for manually assessing morphological features and, in turn, species. This project will build on existing algorithms and apply deep learning to automatically identify various cyanobacteria species and their cell counts from microscopy images. A successful outcome would be extremely beneficial for several organizations, leading to more proactive management of toxic cyanobacteria blooms.

Coastline management using remote sensing and machine learning  

This project will develop a data-driven approach to predict shoreline dynamics based on remotely sensed shoreline data using machine learning algorithms. The ability to forecast coastal shoreline dynamics would facilitate improved coastal management and planning to mitigate both long-term and short-term erosion events through, for example, scheduling and designing of nourishment activities. The Gold Coast beaches provide the perfect natural laboratory due to a wealth of shoreline and oceanographic monitoring data. The shoreline data includes in situ beach profiling and remotely sensed camera and satellite derived shoreline position data. The extensive data available presents a great opportunity to develop the proposed data-driven approach to predict the shoreline dynamics.

Simulating electron transport and plasma physics with high accuracy numerical models

Multiple projects available and can be tailored to applied mathematics, computer science, or physics focus. Central ideas are around construction of simulation frameworks for a variety of high impact plasma and electron transport applications, such as atmospheric lightning discharges on Earth or other planets, low temperature plasma manufacturing processes, astrophysical plasmas, or fusion plasmas. Areas of investigation can be tailored to candidate expertise & interests, including numerical solution techniques for transport equations, the closure problem, machine learning and AI in computational science, kinetic or Monte Carlo methods, or inclusion of accurate atomic and molecular physics data into plasma models.

Plasma modelling and simulation for magnetically confined fusion plasmas

A variety of projects are available in different theory and modelling areas to describe a variety of important physics scenarios important to fusion plasmas, such as those anticipated in tokamaks. Equilibrium plasma discharge, tokamak disruption, runaway electrons, edge-plasma, and surface wall interaction applications are examples of focus applications that are available for study.

Formulation and application of deep learning and other AI techniques to physical sciences

The current wave of deep learning and AI research has yielded many advances in how tools such as neural networks, optimization, or uncertainty quantification can be used to improve modelling capability for a number of useful applications. Projects are available in the development of robust and transparent machine learning and AI techniques that can be employed to augment existing computational modelling techniques (e.g. surrogate models, reduced order models, etc) or to provide new avenues of solution (e.g. PINNs as a famous example).

Modelling electron transport in liquid dark matter and particle detectors

Projects are available in different theory and modelling areas to describe and simulate electron dynamics in liquid forms of rare gases, such as argon, xenon or krypton. This research helps inform the operation, design, and interpretation of large scale dark matter or particle physics detectors that use volumes of liquid as a scintillation background. Understanding how electrons move through these liquids, or even across gas-liquid interfaces, is crucial to identify the detection signals produced by the rare exotic species interacting with the liquid background.

Simulation for design of next-generation electrical switching hardware

In transition to new green energy sources, one thing often ignored is the electricity network infrastructure itself. Most networks rely on a gas insulator sulphur hexafluoride (SF6) - which is about 23,000 times worse than CO2 as a greenhouse gas. Projects are available in different theory and modelling areas to describe and simulate the way discharge arc current forms (and extinguishes) in dielectric gas insulators contained in high voltage hardware. The application of this research is help better understand how to replace SF6, which is the current standard insulating gas used in practically all electrical network hardware.

Novel States for Quantum Memory

This project aims to identify and characterise a previously uninvestigated meta-stable state of Ytterbium with a calculated lifetime of 1 second, making it an idea candidate as a quantum memory as well as holding interest for precision tests of fundemental physics as well as a metrological reference.

Time for Neutrinos

A previous experiment at the ANSTO Lucas Heights nuclear reactor investigated the possibility of neutrino flux altering the flow of time through weak force symmetry violation. This project would consist of analysing that data as well as modelling other situations (low energy solar neutrinos) where a time variation might be induced.

The Fast and The Furious Quantum Gates

Two qubit quantum gates are an essential building block to realising quantum computers. In trapped ion quantum computers these are presently limited by the vibrational confinement of the atoms, however there are proposed protocols which use strong optical forces to surpass this speed limit by using high intensity pulsed lasers. This project would investigate the spectroscopic limits of this approach for several common ions used in trapped ion quantum computing, with the goal of being able to bound the potential maximum fidelity of this approach.

Phototunable Nanophotonic Devices

Optical Metasurfaces, that is 2D arrays of sub-wavelength nanoresonators, have revolutionised the field of photonics by enabling compact, flat and versatile optical devices. The vast majority of optical metasurfaces are based on solid state materials like silicon or TiO2 which cannot be modified after fabrication. This project will explore the application of photoresponsive polymers to modulate the behaviour of optical metasurfaces. Nanoresonators are highly sensitive to surrounding refractive index, so small changes in the polymer property can have a significant effect on the light-matter interaction. In this project, optical metasurface will be coated with photoresponsive polymers, and spatial control over the refractive index will be explored using patterned optical stimulus. The final goal is to develop dynamic photonic devices driven by light.

Environmental sustainability challenges for the air transport industry

Aviation may be economically and socially sustainable but faces real environmental challenges. Globally, emissions from air travel need to be addressed in line with climate change advice from the Intergovernmental Panel on Climate Change (IPCC). There is a need to decarbonise the aviation industry and to move towards Net Zero Emissions (NZE), particularly when demand for international and domestic travel is gaining momentum. The PhD research could focus broadly on the relationship between aviation and climate change, whether mitigation or adaptation. It could alternatively focus on the characteristics of future sustainable air travel, the sustainability strategy of a particular airline or airport, the impact of new technologies (e.g. UAVS, eVTOL) on environmentally sustainable air travel, or the development alternative fuels such as SAF (Sustainable Aviation Fuel), hydrogen or electricity.

Managing waste in the built environment sector

A growing population and urbanisation have led to significant increases in the built environment needs and associated waste generation. There are calls for government and industry to develop better methods in managing waste across the built environment sector, including that from construction and demolition. There are the following strategic priorities: 1) Reducing the impact of waste on the environment, 2) Transitioning to a circular economy for waste, and 3) Building economic opportunity. The PhD research could focus broadly on the relationship between waste and the built environment, or be more specific on a particular built environment sector (e.g. buildings or transport) or waste type (e.g. from Construction & Demolition). It could be quantitative or qualitative in approach. The research could align closely with opportunities associated with the local Queensland Built Environment Waste Strategy and Action Plan, as well as construction around the Brisbane 2032 Olympic and Paralympic Games.

Engineered electron dynamics through controlled light-matter interactions

After absorption of a photon by a molecule an electron is elevated into an electronically excited state and follows the most favourable energetic pathway to either relax back to ground state (fluorescence) or undergo an intersystem crossing into a triplet state where it can initiate a chemical reaction or relax back to the ground state (phosphorescence). What if, instead of leaving these alternate pathways to chance, we could control the specific pathway the electron follows, by enhancing the electric field intensity at specific wavelengths or favour electron intersystem crossing processes with strong magnetic fields. This project will explore the engineering of electric and magnetic fields with dielectric nanoparticles and then bring organic molecules into close proximity and observe the resulting electron dynamics.

Hyperspectral Imagery and Deep Learning for Greenhouse Gas Monitoring

We are seeking a dedicated PhD candidate to contribute to an innovative research project that applies cybersecurity principles to hyperspectral satellite imagery and machine learning. The project focuses on developing secure, data-driven algorithms for safeguarding sensitive environmental data and ensuring the integrity of the monitoring systems. The role will involve designing advanced techniques to process satellite data securely, addressing potential cyber risks in remote sensing applications, and collaborating with industry partners to ensure robust and practical climate change mitigation strategies. Weekly progress reporting and a strong focus on the intersection of cybersecurity and data science make this an ideal opportunity for someone looking to apply their cybersecurity expertise to critical global challenges.

Quantifying the decline of Australia's common species

While global attention has rightly focused on the loss of threatened species, a critical gap exists in quantifying the decline of common species. Common species, often overlooked in conservation priorities, play foundational roles in ecosystems, ensuring the stability of food webs, nutrient cycling, and ecosystem services such as pollination and climate regulation. Yet, evidence shows that many of these once-abundant species are experiencing significant population declines due to habitat loss, climate change, pollution, and human activity. This project aims to investigate which species have declined, where this decline has occurred, and potential drivers of decline.

Spatially planning for the expansion of plantations

As the call to end native forest logging grows louder due to its detrimental impacts on biodiversity, carbon storage, and ecosystems, the transition to plantation-based timber production becomes a critical pathway. However, this shift demands more research into where we should expand plantations and how to manage them sustainably. Current knowledge gaps include identifying optimal locations for plantation expansion that minimize biodiversity loss and land-use conflicts while maximizing carbon sequestration. Equally important is developing sustainable management practices that avoid the pitfalls of monocultures, promote biodiversity within plantations, and maintain soil health and water quality.

Mapping the Path to Koala Recovery through the Great Koala National Park

The New South Wales Government’s commitment to creating the Great Koala National Park represents a landmark opportunity to reverse the rapid decline of koalas and protect their habitats. However, critical knowledge gaps remain regarding the spatial extent, specific locations, and conservation actions needed to halt koala extinction and put them on a path to recovery. This research project aims to provide a spatial plan to ensure the park’s success in conserving koalas. By mapping critical koala habitats and assessing threats such as deforestation, disease, and climate change, we will identify priority areas for protection, management and restoration. Additionally, we will evaluate the specific actions needed, such as habitat corridors, predator control, and disease management, to bolster koala populations. Beyond direct koala conservation, the Great Koala National Park offers substantial co-benefits that warrant quantification. These include enhanced biodiversity conservation by protecting habitats for other threatened species, increased carbon sequestration through forest preservation and restoration, and economic benefits from eco-tourism.

Charting Australia's Path to 30% by 2030

Australia’s commitment to protecting 30% of its land and sea areas by 2030, in line with global biodiversity targets, represents a crucial step toward halting biodiversity loss and restoring ecosystems. However, the roadmap to achieving this ambitious goal remains unclear. Critical questions about the location of new protected areas, the integration of Indigenous land management practices, and the economic and social implications of expanding protected areas need to be addressed through robust research. This research project aims to investigate the pathways for Australia to achieve the 30% by 2030 target by identifying priority areas for protection. A key focus will be on balancing biodiversity conservation with other land uses, such as agriculture, forestry, and urban development, to ensure the transition is equitable and sustainable.

Identifying Species at Risk of Losing All Habitats Due to Climate Change

Climate change is rapidly altering ecosystems, pushing many species to the brink of extinction as their habitats shrink or disappear entirely. However, a critical gap in our understanding remains: which species are most at risk of having no viable habitats in the future due to rising temperatures, shifting rainfall patterns, and other climate-related changes? This research aims to address this urgent question by identifying species likely to face complete habitat loss by the end of the century. Using advanced climate modeling, species distribution data, and habitat suitability analysis, we will predict which species will be unable to find suitable environments as climate change intensifies. This research will focus on species with narrow habitat ranges, specialized ecological needs, and limited dispersal abilities, which make them particularly vulnerable.

Vibration analysis of multilayer panels with auxetic cores

Auxetic structures are gaining attention for their adjustable properties in varying conditions, serving as a core in sandwich structures to mitigate energy transfer during impact, shock loading, crushing, and bending. These structures, conducive to personal protection equipment and protective constructs, are being developed using modern additive manufacturing. The project aims to analyse the acoustic and vibration response of an auxetic core in a multilayer structure through finite element method. Numerical modelling of 3D printed parts will involve establishing a constitutive relationship and considering material properties to investigate the eigenvalue problem.

Elastostatic analysis of multilayer panels with auxetic cores

Auxetic structures are gaining attention for their adjustable properties in varying conditions, serving as a core in sandwich structures to mitigate energy transfer during impact, shock loading, crushing, and bending. These structures, conducive to personal protection equipment and protective constructs, are being developed using modern additive manufacturing. The project aims to create efficient sandwich composite with an auxetic core through novel designs, optimizing their performance using finite element tools. Numerical modelling of 3D printed parts will involve establishing a constitutive relationship, considering material properties at different strain rates and temperatures to enhance the efficiency of the material model. This approach enhances load-bearing capabilities and provides a predictive tool for future researchers analysing the material response of additively manufactured components.

Enhancing Cybersecurity with Generative AI

This project focuses on the integration of Generative AI into cyber resilience strategies to enhance an organization's ability to anticipate, withstand, and recover from cyber threats.

Measuring and modelling human central pattern generators in the context of spinal cord injury

Spinal cord injury is a severe personal injury. PRECISE's BioSpine team have developed non-invasive therapeutic technology to faciliate recovery of sensorimotor function after spinal cord injury. As part of this larger project, we aim to gain better understanding of how the human central pattern generators might be modulated through BioSpine therapy.

Towards controlling bone mechanics during rehabiliation following spinal cord injury

Spinal cord injury is a severe personal injury. PRECISE's BioSpine team have developed non-invasive therapeutic technology to faciliate recovery of sensorimotor function after spinal cord injury. As part of this larger project, we aim to create a technological framework to enable control of the rehabiliation such that it promotes healthy bone adaptation and lowers risk of bone injury.

Towards an interactive human-machine software to control joint loading in those with hip osteoarthritis

Hip osteoarthritis is a major cause of disability in Australia, with growing prevalence in our aging population. Controlling tissue loading is a promising non-invasive method to manage hip osteoarthritis. PRECISE researchers have developed wearable technology to enable monitoring and control of hip tissue loading during daily activities outside the laboratory (e.g. at home). However, this technology requires development and testing of user interface software to ensure the technology is effective in use and acceptable by users.

BioMotionAI: Towards a clinic-friendly tool to estimate musculoskeletal tissue loads

Musculoskeletal conditions are the single largest cause of disability globally. New research technologies can monitor and control musculoskeletal tissues to prevent their injury and promote healing in a laboratory setting. However, to be effective, these technologies must be translated into clinics and homes. PRECISE researchers are developing a computer vision based technology that will function in allied health clinics to provide insights into musculoskeletal tissue loading in a clinical setting.

Preventing injuries in female Australian Footballers

Female participation in Australian Football is rising at a rapid rate. So too are the rates of severe musculoskeletal injuries in this population. The causes of these injuries remains contentious in the research community. PRECISE researchers have developed a set of predictive tools that can estimate risk of tissue injuries with high levels of certainty. What remains to be established is what interventions can be performed to mitigate these risks based on mechanistic understanding of tissue injury.

Understanding and augmenting human foot function to improve musculoskeletal health and physical performance across the lifespan

Recent advances in dynamic radiography and computational modelling has radically challenged conventional understanding of foot shape and function. PRECISE researchers are pushing the boundaries of knowledge regarding the evolution of the human foot and its contributions to human performance and dysfunction.

Towards optimised paediatric orthopaedic surgery using in silico simulation, planning, and 3D printed surgical kits

Personalised digital twins of a patient's neuromusculoskeletal system enables accurate in silico planning, simulation, and surgical translation for a variety of conditions from paediatric anterior cruciate ligament reconstruction to proximal femoral osteotomy to adult reconstruction of the scapholunate interosseus ligament. PRECISE researchers have developed platform technology to conduct research in this field.

Towards physics-informed artificial intelligence to predict tissue, organ, and body mechanics in healthy and diseased populations

Precision modelling for health and peformance holds great promise. However, bringing these research technologies to the real-world to create impact requires development of methods that enable an "escape from the laboratory". The combination of wearable devices with physics-informed artificial intelligence is emerging as a promising approach to achieve field-friendly technology that retains accuracy and reliability.