Locations

AI for Early Detection of Neurodegenerative Disorder in Elderly

This Project aims to develop outcome measures for early detection of neurodegenerative disorders using artificial intelligence (AI) and various sensing modalities, offering personalised support to older adults. The expected outcome is the creation of AI algorithms to detect early signs of neurodegenerative disorders in older adults living independently at home. The potential benefit is early detection of neurodegenerative disorders in older adults, improving quality of life and effective disease management.

Ecological drivers of disease spread in horticultural tree crops

This project investigates ecological interactions that influence disease spread in tree crop horticultural systems. The expected outcomes are improved understanding of ecological drivers of the dynamics of diseases and ecological intervention/restoration strategies for disease management. The potential benefit is chemically limited sustainable disease management in horticulture, benefiting industry and the environment.

Hydrogels with mechanical properties for 3D in vitro cell models

This Project combines different polymer chemistries to develop hydrogels that can be made stiffer or softer on-demand, replicating physiological processes. The expected outcome is the creation of hydrogels for 3D cell culture that better mimic native tissues in different stages of their development and disease. The potential benefit is improved in vitro/non-animal models with lower attrition rates and cost in drug discovery and development of advanced therapies.

AI motion capture system for enhancing human motor function

This project aims to advance the field of human movement science by addressing the challenges encountered when developing a low-cost, automated system for screening the movement of pre-elite student-athletes. Leveraging state-of-the-art artificial intelligence (AI), markerless motion capture and stereo vision technologies, this research will tackle critical challenges in biomechanics and sports science.

Influencing factors of feed intake and digestion in prawns

This Project will investigate poorly understood mechanisms regulating feed consumption in prawns through a multi-disciplinary approach studying feeding behaviour, digestive physiology, nutritional needs and metabolism regulation. The expected outcomes are to better understand dietary factors and physiological mechanisms promoting feed consumption and return of appetite in juvenile prawns. This may lead to the application of new feed and feeding strategies to enhance aquaculture performance and sustainability.  

Optimizing carcase quality and animal welfare

This Project will investigate stunning factors in commercial beef processing facilities that may influence carcase quality, meat quality and/or animal welfare. The expected outcomes are to identify risk factors associated with stunning that influence meat quality and to support the claim that the Australian beef processing sector is continuing to improve welfare outcomes. This could provide evidence to bolster the ongoing social acceptance of the beef industry.

Prediction of molecular interactions with RNA using AI

This Project will develop deep-learning models to predict interactions of ribonucleic acid (RNA) with other molecules. The expected outcomes are to improve prediction capabilities to decode RNA interactions in disease mechanisms, identify novel therapeutic modalities, and improve existing therapies for targeting RNA. This could result in enhanced capacity to design new therapies and potential to optimise RNA targeting molecules for therapeutic applications. 

Applying imaging methods and data analytics to explore the listening brain

This Project aims to understand brain circuits and processes supporting communication in individuals with hearing problems, including those who use devices such as hearing aids and cochlear implants. The potential benefits are that individualised strategies based on real-time brain states estimate algorithms to empower listening and support effective communication. The Project will use brain-imaging techniques‚ including those compatible with listening technologies, including electroencephalogram (EEG), to explore the listening brain. The Project will explore brain changes that arise from hearing loss, how changes in brain function – within and beyond the auditory brain – arise to support listening when hearing is impaired, and how these findings can be used as a part of devices such as cochlear implants that engage the rest of the brain to support an individual's listening. 

Synthetic CT via Generative AI for MR-guided Radiotherapy Planning in the Abdomen and Lungs

This Project will leverage artificial intelligence to develop and validate synthetic computed tomography (CT) from magnetic resonance imaging (MRI) in the abdominal and lung regions. The expected outcomes are an AI-based synthetic CT model, thorough technical and clinical validation and potential patent/licensing opportunities. This may reduce unnecessary ionising radiation of CT in patients and improve treatment efficiency during radiotherapy planning.

Clinical lab automation with AI human robot interface

This Project will develop an AI-based robotic programming interface based on large-language model that allows practitioners, regardless of their technical expertise, to efficiently program and control robots. The expected outcomes are to improve efficiency in designing and deploying clinical lab automation and to expand the use of robotics within laboratories. This may lead to improvements with workflow for clinical lab automation, particularly during high-demand situations like pandemic outbreaks.