Discovery Early Career Researcher Award - Grant ID: DE220100265
Funder
Australian Research Council
Funding Amount
$417,000.00
Summary
A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and ....A closed-loop human–agent learning framework to enhance decision making. This project aims to design a foundational human–agent learning framework to augment the decision making process, using reinforcement and closed-loop mechanisms to enable symbiosis between a human and an artificial-intelligence agent. It envisages significant new technologies to promote controllability and efficient and safe exploration of an environment for decision actions – drastically boosting learning effectiveness and interpretability in decision making. Expected outcomes will benefit national cybersecurity by improving our understanding of vulnerabilities and threats involving decision actions, and by ensuring that human feedback and evaluations can help prevent catastrophic events in explorations of dynamic and complex environments.Read moreRead less
A Midas touch for electrophiles in new reaction development. This project aims to address the lack of knowledge about how high-value organic molecules are formed in gold-catalysed reactions by advancing a novel mode of catalysis. This project expects to generate new knowledge about these gold-catalysed reactions using an innovative, interdisciplinary approach incorporating computational and synthetic techniques. Expected outcomes of this project include the optimisation and development of import ....A Midas touch for electrophiles in new reaction development. This project aims to address the lack of knowledge about how high-value organic molecules are formed in gold-catalysed reactions by advancing a novel mode of catalysis. This project expects to generate new knowledge about these gold-catalysed reactions using an innovative, interdisciplinary approach incorporating computational and synthetic techniques. Expected outcomes of this project include the optimisation and development of important organic reactions and enhancing collaboration nationally and internationally between computational and synthetic chemists. This should provide significant benefits in the form of improved chemical reactions for chemists to prepare new pharmaceuticals, agrochemicals and materials.Read moreRead less
Switchable and stereocontrolled photoredox catalysis. This project aims to develop new catalytic synthetic reactions for the rapid and more direct functionalisation of organic compounds under mild conditions with the use of visible light. An integrated experimental and computational approach will be used to design potent visible-light photocatalysts that retain the advantages of standard photoredox catalysis but with the added ability to intercept and, thus control, reactive intermediates in sit ....Switchable and stereocontrolled photoredox catalysis. This project aims to develop new catalytic synthetic reactions for the rapid and more direct functionalisation of organic compounds under mild conditions with the use of visible light. An integrated experimental and computational approach will be used to design potent visible-light photocatalysts that retain the advantages of standard photoredox catalysis but with the added ability to intercept and, thus control, reactive intermediates in situ. This will enable the control of stereochemistry in photoredox reactions – not possible with standard catalysts - and establish other useful synthetic transformations. These strategies will make it easier to prepare valuable classes of organic molecules – efficiently, safely, and cost-effectively.
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Managing infectious disease through partial wildlife social networks. This project aims to investigate the dynamics of the spread of infectious disease in wildlife, derived from incomplete information about contact networks. Infectious diseases in wildlife are difficult to track and control, because it is not feasible to monitor each individual in a population and know the contact network for a population. The project will create ways to best utilise incomplete observational data of contact netw ....Managing infectious disease through partial wildlife social networks. This project aims to investigate the dynamics of the spread of infectious disease in wildlife, derived from incomplete information about contact networks. Infectious diseases in wildlife are difficult to track and control, because it is not feasible to monitor each individual in a population and know the contact network for a population. The project will create ways to best utilise incomplete observational data of contact networks to develop robust predictions of disease spread and population fate, and to reliably predict the outcomes of management interventions. These robust prediction methods will provide better insights for conservation of Australian wildlife.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH180100002
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. Thes ....ARC Research Hub for Driving Farming Productivity and Disease Prevention. The ARC Research Hub for Driving Farming Productivity and Disease Prevention aims to increase farm production and disease prevention through advancing and transferring new artificial intelligence technologies into industrial deployment. The Hub will combine machine vision, machine learning, software quality control, engineering, biology, and farming industries to develop technologies to build more intelligent systems. These dynamic systems will help determine what goal to achieve and the most efficient plan to achieve it. This Hub is expected to contribute to higher farming efficiency, lower production costs and fewer disease risks, giving the Australian industry new business opportunities and an international competitive advantage.Read moreRead less