Scholarship opportunities

Enhancing cybersecurity with AI and Large Language Models 

This project will explore the integration of artificial intelligence (AI) and large language models (LLMs) to predict organisational cybersecurity risks and mitigate threats in advance. The expected outcomes are an enhanced cybersecurity framework, better threat intelligence techniques and user-centric designs, and an adaptable solution. This may help businesses to identify cyber risks and prevent cyber incidents prior to happening and avoid financial losses and brand damage.   

Extending the shelf life of UHT plant protein beverages

This Project aims to improve understanding and overcome the negative effects of the secondary lipid oxidation products and Maillard reaction in UHT plant protein beverages. The expected outcome is a methodology to impede the negative impact of malodorous/browning reactions in high protein UHT beverages. This may lead to the extension of shelf life of these products furthering export opportunities.  

Evaluating Robotic Medical Surgery with Multimodal and Responsible AI

This Project aims to develop multimodal and responsible artificial intelligence (AI) for automated robotic surgery assessment. The expected outcome is to develop multimodal and responsible AI for automated robotic surgery assessment. The potential benefit is enhanced surgical training, improved patient outcomes, reduced training costs, and increased transparency. 

Shaping human and AI collaboration in Security Operations Centres

This project investigates innovative solutions to enhance collaborative intelligence, leveraging human and artificial intelligence affordances, in security operations centres (SOC). The expected outcome is the development of socio-technical artifacts that leverage cyber threat intelligence for enhanced cyber situational awareness and sensemaking in SOCs. The potential benefit is faster and more effective responses to cyber threats in Australia. 

Fingerprinting critical minerals development in continental margins

This project will study how critical minerals develop in the Andean-type plate margins through case studies in eastern Australia. The expected outcomes are results that characterise signature minerals in a polymetallic minerals province in Far North Queensland. This may result in supplying new data-driven mineralisation models for key exploration regions.

Li-ion battery separator material recovery and utilisation

Sodium battery is a promising alternative for energy storage if precious metal prices for making LIB remain high. This Project will mainly focus on the recovery of LIB separator material and explore economic applications of the recovered separator materials, such as turning it into high-value hard carbon for making sodium battery anode material.

Crop disease management

This project aims to integrate novel automated disease surveillance technology into real-time management of crop diseases for improved yield and sustainability outcomes. The expected outcome is grower recommendations for crop diseases informed by real-time automated disease surveillance. The potential benefit is improved productivity and sustainability through reduced crop losses and chemical inputs.

Data analytics for enhanced hearing health

This project aims to consolidate hearing health data with other datasets to unlock valuable insights and drive personalised hearing healthcare interventions. The expected outcomes are to streamline access to hearing health data, improve data connectivity, and enhance treatment outcomes. The potential benefit is to facilitate informed decision-making for healthcare providers to enable better patient care.

Communicating in the real world

This project aims to utilise existing sensor technologies to evaluate the impact of hearing loss when communicating in noisy environments. The expected outcome is the discovery of novel analysis methods that utilise multi-sensor fusion techniques. The potential benefit is improved outcomes for individuals with hearing loss.

Optimising vegetation management and grazing in solar farms

This project aims to use high spatial, spectral and temporal resolution satellite images and artificial intelligence to derive measurements that enhance vegetation management and grazing efficiency in solar farms. The expected outcome is to develop algorithms that can be applied to near real-time satellite data to enable the spatially comprehensive measurements of vegetation growth, grazing patterns overgrazing, curing rating for decision making in monitoring and determining the effectiveness of land management strategies.