VIC

Quantifying methane emissions from wastewater treatment

This project aims to quantify methane emissions from wastewater treatment plants. The expected outcomes are improved understanding of methane emissions from within the plant, their spatial and temporal variability, and how they contribute to the total emissions. This may reduce emissions of methane.  

Cyber security risk mitigation for sensor data integrity

This Project will investigate cyber security risk mitigation approaches to secure the integrity of sensor information feeding into critical infrastructure operational systems and digital twins. The expected outcome is the development of guidelines for implementing robust cyber security measures. This may enhance resilience against cyber threats and ensure the integrity of decision-making processes.​

Resilient and quantum-safe threshold cryptography

This Project will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold encryption and/or authentication algorithms. The potential benefit is to enhance the security of Australian critical infrastructures, safeguarding them against quantum attacks.

Enabling Post-Quantum Cryptography (PQC) Migration

This Project develops techniques for the migration to post-quantum cryptography (PQC) to secure critical infrastructure from quantum attacks. The expected outcome is the design of methods, techniques and their prototype to implement trusted PQC migration. The potential benefit is to enhance the security of Australian critical infrastructures against quantum attacks

Satellite-based Methane Detection

Methane is a potent greenhouse gas and an important contributor to climate change. This project will develop neural network-based methods to detect anthropogenic methane plumes in satellite imagery and quantify emission rates. The expected outcomes are better detection and monitoring of methane emissions in Australia compared to current methods, with enhanced temporal and spatial coverage. These advancements will enhance Australia’s capability to efficiently identify, quantify and mitigate methane emissions. 

A cotton pangenome for enhanced genomic selection 

This Project will identify key representative lines of the Australian cotton breeding population to construct a cotton pangenome. The expected outcome is to construct a cotton pangenome using the lines which are most efficient in terms of labour and financial resources. This may help increase the accuracy of genomic selection and hence increase the rate of genetic gain. 

Repurposing & fortification of nutrient-rich waste streams

This project investigates the potential of the combined waste streams from the two industries to develop new ingredients for food applications. The expected outcome is a framework to develop products from reclaimed and fortified nutrient-rich fractions suited to their physico-chemical properties. The potential benefit is improved nutrient upcycling, reduced food waste and carbon footprint. 

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.