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
Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical application ....Context and Activity Recognition for Personalised Behaviour Recommendation. The Internet of Things (IoT) together with the rising popularity of smartphones opens a new world for many exciting opportunities. The overall goal of this project is to develop new algorithms and data analytical techniques in an IoT environment that can accurately monitor and analyse personalised daily activities on a continuous, real-time basis. The expected result of this project will support many critical applications such as better wellness tracking and lifestyle-related illness prevention, which will be particularly critical to Australia's aging population. This project will also serve as a vehicle to educate and train Australia’s young scholars and engineers.Read moreRead less
Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human expl ....Robust Preference Inference from Spatial-Temporal Interaction Networks. This project aims to develop innovative techniques for effectively and efficiently managing user preference profiles from less labelled, sparse and noisy interaction data. A unified novel learning framework along with a set of data analysis techniques are expected to be developed from this project, which will provide a non-intrusive way of conducting predictive analysis on user preference profiling via discovering human explicit and implicit interest domains. The expected results of this application will not only maintain Australia's leadership in this frontier research area, but also support many important applications that safeguard Australian people and economy such as cyber security, healthcare, and e-Commerce.Read moreRead less
Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance witho ....Automatic Training Data Search and Model Evaluation by Measuring Domain Gap. We aim to investigate computer vision training data and test data, using automatically generated data sets for facial expression recognition and object re-identification. This project expects to quantify and understand the domain gap, the distribution difference between training and test data sets. Expected outcomes of this project are insights on measuring the domain gap, the ability to estimate model performance without accessing expensive test labels and improvements to system generalisation. This should provide significant benefits for computer vision applications that currently require expensive labelling, and commercial and economic benefits across sectors such as transportation, security and manufacturing.Read moreRead less
Physico-chemical effects on long-time fluid transport for CO2 geostorage. This project aims to develop an efficient multi-scale laboratory-based modelling framework for the analysis of nonequilibrium transport and reaction processes occurring in CO2 storage scenarios. In a significant technological advance two non-destructive analysis techniques, Xray computed tomography and nuclear magnetic resonance, are combined with pore-scale simulations to address uncertainties in dynamic wettability alter ....Physico-chemical effects on long-time fluid transport for CO2 geostorage. This project aims to develop an efficient multi-scale laboratory-based modelling framework for the analysis of nonequilibrium transport and reaction processes occurring in CO2 storage scenarios. In a significant technological advance two non-destructive analysis techniques, Xray computed tomography and nuclear magnetic resonance, are combined with pore-scale simulations to address uncertainties in dynamic wettability alteration occurring during gravity driven convection. Expected outcomes are the in-situ characterisation of solid-surface interactions and predictions of multi-phase fluid flow. The project benefits the Australian resources sector by improving injectivity, storage efficiency and security of supercritical CO2 storage projects.Read moreRead less
Socially Responsible Insurance in the Age of Artificial Intelligence. This project aims to discover the social costs and benefits of using Artificial Intelligence in insurance, and to design practical interventions—responsible design workshops, practical guidance, regulatory proposals, new algorithmic tools—that realise the benefits while mitigating the costs. It expects to generate new knowledge drawing on philosophy, law and sociology, working closely with practitioners at the forefront of dep ....Socially Responsible Insurance in the Age of Artificial Intelligence. This project aims to discover the social costs and benefits of using Artificial Intelligence in insurance, and to design practical interventions—responsible design workshops, practical guidance, regulatory proposals, new algorithmic tools—that realise the benefits while mitigating the costs. It expects to generate new knowledge drawing on philosophy, law and sociology, working closely with practitioners at the forefront of deploying AI in insurance. Expected outcomes include novel ethical AI-based approaches to product design, pricing and claims administration. This should benefit insurers and consumers, realising efficiency gains made possible by AI, without unacceptable costs to privacy, fairness, and the unaccountable exercise of power. Read moreRead less
Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, ....Uncertainty, Risk and Related Concepts in Machine Learning. Machine learning is the science of making sense of data. It does not and cannot remove all risk and uncertainty. This project proposes to study the foundations of how machine learning uses, represents and communicates risk and uncertainty. It aims to do so by finding new theoretical connections between diverse notions that have arisen in allied disciplines. These include risk, uncertainty, scoring rules and loss functions, divergences, statistics and different ways of aggregating information. By building a more complete theoretical map it is expected that new machine learning methods will be developed, but more importantly that machine learning will be able to be better integrated into larger socio-technical systems.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
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE170100033
Funder
Australian Research Council
Funding Amount
$900,000.00
Summary
Australian Acoustic Observatory: A network to monitor biodiversity. This project aims to create a terrestrial acoustic sensor network comprising 450 listening stations across Australia. Acoustic sensing transforms environmental science by recording vocal species 24/7, providing spatial and temporal data for ecosystem monitoring and research. Australia has leading research expertise in this emerging field, which is relevant to its fragile and mega-diverse environment. This project is expected to ....Australian Acoustic Observatory: A network to monitor biodiversity. This project aims to create a terrestrial acoustic sensor network comprising 450 listening stations across Australia. Acoustic sensing transforms environmental science by recording vocal species 24/7, providing spatial and temporal data for ecosystem monitoring and research. Australia has leading research expertise in this emerging field, which is relevant to its fragile and mega-diverse environment. This project is expected to enable and develop continental scale environmental monitoring, and the data generated will be made freely available to all online, enabling new science in understanding ecosystems, long-term environmental change, data visualisation and acoustic science.Read moreRead less