Using data mining methods to remove uncertainties in sensor data streams. This project will develop key techniques for removing uncertainties in sensor data streams and thus improve the monitoring quality of sensor networks. The expected outcomes will benefit Australia by enabling improved, lower-cost monitoring of natural resources and management of stock raising.
ARC Centre of Excellence for Carbon Science and Innovation. ARC Centre of Excellence for Carbon Science and Innovation. This Centre aims to develop carbon-based catalysts for clean energy, CO2 capture, and green chemistry to reduce emissions. The Centre expects to use pioneering data-guided atomic-precision synthesis and multiscale analysis to transform fundamental science of carbon materials. Expected outcomes of this Centre will benefit new technologies for energy, environmental, and green che ....ARC Centre of Excellence for Carbon Science and Innovation. ARC Centre of Excellence for Carbon Science and Innovation. This Centre aims to develop carbon-based catalysts for clean energy, CO2 capture, and green chemistry to reduce emissions. The Centre expects to use pioneering data-guided atomic-precision synthesis and multiscale analysis to transform fundamental science of carbon materials. Expected outcomes of this Centre will benefit new technologies for energy, environmental, and green chemical industries by utilising abundant sunlight, seawater, and waste feedstocks. This should provide significant benefits, through industry collaborations, our new world-leading capacity will train a next generation of game changers to empower emerging carbon industries to solve grand socio-economic challenges, ultimately meeting zero-carbon emissions targets.Read moreRead less
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
Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data ar ....Visual analytics for massive multivariate networks. Visual analytics for massive multivariate networks. This project aims to create methods to visually analyse massive multivariate networks. The amount of network data available has exploded in recent years: software systems, social networks and biological systems have millions of nodes and billions of edges with multivariate attributes. Their size and complexity makes these data sets hard to exploit. More efficient ways to understand the data are needed. This project will design, implement and evaluate visualisation methods for massive multivariate network data sets. This research is expected to be used by Australian software development, biotechnology and security companies to exploit their data.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH220100012
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
ARC Research Hub for Carbon Utilisation and Recycling. This Research Hub aims to develop technologies to transform carbon dioxide emissions from our energy and manufacturing sectors into valuable products and create pathways to market to drive industry transformation. This hub aims to achieve this by developing novel electro, thermo, and biochemical methods for converting CO2 from sectors that cannot easily avoid emissions and a technological pathway for CO2 recycling. The outcomes of this Hub a ....ARC Research Hub for Carbon Utilisation and Recycling. This Research Hub aims to develop technologies to transform carbon dioxide emissions from our energy and manufacturing sectors into valuable products and create pathways to market to drive industry transformation. This hub aims to achieve this by developing novel electro, thermo, and biochemical methods for converting CO2 from sectors that cannot easily avoid emissions and a technological pathway for CO2 recycling. The outcomes of this Hub are likely to be transformative for industry, the economy, and society in moving the fate of CO2 from pollutant to feedstock. The benefits to Australia are intended to be the stimulation of a new industry, a skilled workforce for this emerging industry and a contribution to meeting CO2 reduction targets.Read moreRead less