Call for Joint Research Projects

This project will create Australia’s first “Digital Twin” of population-level physical activity, sedentary behaviour, and associated health outcomes. Drawing on wearable device data, national health surveys, and environmental mapping, AI models will simulate how changes in policy, infrastructure, and public health interventions affect physical activity and chronic disease prevalence. ...Read More

This project will integrate wearable-derived data on sleep, activity, and nutrition into an AI-powered adolescent health risk model. The system will detect early metabolic risk factors, such as insulin resistance, and deliver tailored school-based interventions supported by teachers, parents, and health professionals...Read More

This project will tackle the growing issue of excessive smartphone use among high school students, which is linked to reduced physical activity, poor sleep, and declining academic performance. Using AI algorithms, smartphone usage patterns will be analysed in real time and linked to wearable sensor data measuring activity, sleep, and mood. The platform will deliver personalised behavioural prompts and challenges, aiming to reduce screen time and improve overall wellbeing....Read More

Co-designed with Aboriginal and Torres Strait Islander communities, this project will develop a culturally safe, AI-driven health platform to prevent and manage diabetes and related chronic conditions. The system will integrate cultural determinants of health, community input, and clinical data, producing tailored prevention and care pathways. It will be deployed via mobile health applications and supported by Aboriginal Community Controlled Health Organisations (ACCHOs) and regional health services....Read More

This project will integrate genomic, biomarker, clinical, and lifestyle data from diverse Australian populations into an AI-powered predictive model for Type 2 Diabetes (T2D) risk. The platform will enable early identification of high-risk individuals and provide personalised care plans embedded directly into GP practice software and hospital electronic records...Read More

This project targets β-cell dysfunction, a central driver of both Type 1 and Type 2 Diabetes progression, by uniting experts from clinical medicine, bioinformatics, immunology, artificial intelligence, molecular biology, and translational therapeutics. Using multi-omic datasets from human cohorts and experimental models, advanced AI algorithms will identify molecular signatures, immune interactions, and metabolic stress pathways contributing to β-cell failure. Insights will be validated in preclinical systems and linked to clinical phenotypes, enabling the development of targeted therapeutic strategies....Read More

This project will use AI to investigate the genetic and metabolic mechanisms governing energy utilisation and insulin resistance in Type 2 Diabetes (T2D). By integrating genomic sequencing, mitochondrial function analysis, and metabolic flux data, the research will identify gene–environment interactions that influence metabolic efficiency and glucose regulation....Read More

This project will apply AI to unravel the complex inflammatory pathways driving Type 2 Diabetes (T2D) and its complications, with the goal of identifying novel therapeutic targets. By integrating multi-omic datasets — including transcriptomics, proteomics, and metabolomics — from patient cohorts and experimental models, the research will reveal critical molecular drivers of metabolic inflammation. Machine learning will be used to map these pathways, highlight key intervention points, and prioritise drug targets. Collaborations with pharmaceutical partners and Helmholtz Munich will facilitate preclinical validation and clinical translation....Read More

This project will co-design culturally adapted AI tools for the prevention and management of Type 2 Diabetes (T2D) in Aboriginal and Torres Strait Islander communities. Developed in partnership with Aboriginal Community Controlled Health Organisations (ACCHOs), Indigenous health workers, and community leaders, the AI platform will integrate clinical, community, and social determinants data to create culturally relevant risk prediction models...Read More

This project will harness the power of AI to analyse large-scale longitudinal datasets and predict disease progression and treatment outcomes in Type 2 Diabetes (T2D). Drawing on decades of data from Australian and international cohorts, including lifestyle, clinical, biochemical, and genetic information, the project will identify distinct disease trajectories and the factors influencing rapid progression or remission....Read More