Enabling wider use of mechanistic models for biodiversity forecasts . Forecasting species distributions is challenging yet necessary. The pattern-based models commonly used are error-prone. Mechanistic models, best equipped for the task, are limited by lack of data. This project aims to enable wider use of mechanistic models by developing new methods for dealing with incomplete trait data and uncertainty. It expects to generate new knowledge about how species’ traits define the environments in w ....Enabling wider use of mechanistic models for biodiversity forecasts . Forecasting species distributions is challenging yet necessary. The pattern-based models commonly used are error-prone. Mechanistic models, best equipped for the task, are limited by lack of data. This project aims to enable wider use of mechanistic models by developing new methods for dealing with incomplete trait data and uncertainty. It expects to generate new knowledge about how species’ traits define the environments in which they persist. Anticipated outcomes include enhanced capacity to apply mechanistic models to conservation problems, methods for communicating uncertainties and models for tens of species of immediate conservation interest. This will enable more reliable biodiversity forecasts, supporting better decision-making.
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Personalised public transport. This project aims to address urban congestion by utilising people’s travel plans to coordinate journeys. The project expects to generate new knowledge in scalable optimisation, based on innovative modelling of urban transport, and tested on historical data from Melbourne. The expected outcomes of the project are an active transport database and optimised mode choice and routing system, with predicted reductions in congestion based on simulation of its use. This pro ....Personalised public transport. This project aims to address urban congestion by utilising people’s travel plans to coordinate journeys. The project expects to generate new knowledge in scalable optimisation, based on innovative modelling of urban transport, and tested on historical data from Melbourne. The expected outcomes of the project are an active transport database and optimised mode choice and routing system, with predicted reductions in congestion based on simulation of its use. This project aims to design an urban trip advisory system that could be followed by automated vehicles as well as human drivers, to reduce the financial and environmental cost of current urban congestion.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100477
Funder
Australian Research Council
Funding Amount
$449,592.00
Summary
Quantifying climate change impacts for wetlands in agricultural landscapes. This project aims to quantify the impacts of changed water availability on wetland biodiversity. Research will focus on high conservation value wetlands in agricultural regions, which face significant climatic risk. Novel integration of biodiversity theory with hydroecological and spatial modelling is expected to generate new understanding of how water availability drives wetland diversity. Intended outcomes include new ....Quantifying climate change impacts for wetlands in agricultural landscapes. This project aims to quantify the impacts of changed water availability on wetland biodiversity. Research will focus on high conservation value wetlands in agricultural regions, which face significant climatic risk. Novel integration of biodiversity theory with hydroecological and spatial modelling is expected to generate new understanding of how water availability drives wetland diversity. Intended outcomes include new techniques to model wetland biodiversity, building of international collaborations and enhanced ability to support policy development to ameliorate climate-related wetland impacts. This should promote sustainable management of water and biodiversity in farmlands, benefitting productive capacity and environmental amenity.Read moreRead less
Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended ....Data-driven Traffic Analytics for Incident Analysis and Management. Traffic incidents are among the primary concerns of all transport authorities around the world due to their significant impact in terms of traffic congestion and delay, air and noise pollution, and management cost. This project aims to address incident analysis and management in complex and multi-modal traffic networks by combining multidisciplinary research efforts from transportation engineering and data science. The intended outcomes will be an innovative incident analysis and management framework synergising traffic data analytics and traffic simulation modelling as well as its key enabling techniques and prototype systems. This will significantly help mitigate incident impacts on daily commuters.Read moreRead less
Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it i ....Robust, valid and interpretable deep learning for quantitative imaging. One of the biggest challenges in employing artificial intelligence is the “black-box” nature of the models used. This project aims to improve the effectiveness and trustworthiness of deep learning within quantitative magnetic resonance imaging. Deep learning has great promise in speeding-up complex image processing tasks, but currently suffers from variable data inputs, predictions are not guaranteed to be plausible and it is not clear to the end user how reliable the results are. The outcomes intend to deliver advanced knowledge and capability in artificial intelligence and machine learning that Australia urgently needs to capitalise on bringing deep learning into practical applications delivering economic, commercial and social impact.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE200100892
Funder
Australian Research Council
Funding Amount
$419,889.00
Summary
Next-generation, prefabricated, modular, solar heating and cooling system. This project aims to develop a new window design that can reduce the heating of buildings caused by the sun in warm weather and reduce heat loss from buildings in cool weather. This project expects to generate new knowledge on the interaction between solar radiation and the convection of air inside a cavity within the window design. The expected outcome is a framework that can be used to optimize window designs for buildi ....Next-generation, prefabricated, modular, solar heating and cooling system. This project aims to develop a new window design that can reduce the heating of buildings caused by the sun in warm weather and reduce heat loss from buildings in cool weather. This project expects to generate new knowledge on the interaction between solar radiation and the convection of air inside a cavity within the window design. The expected outcome is a framework that can be used to optimize window designs for buildings under various weather conditions. This should allow quick and easy fabrication and implementation of the designs in existing and new buildings, and the windows should significantly reduce building heating and cooling costs.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101549
Funder
Australian Research Council
Funding Amount
$395,775.00
Summary
A virtual platform for animal–human inhalation toxicity extrapolation. This project aims to remove the long-lasting barrier in extrapolating data from animals to humans by developing an integrated virtual platform. This project expects to fully resolve inhalation exposure differences in nasal airways between commonly used animal surrogates and humans, which could lay scientific underpinnings in developing rigorous interspecies data conversion schemes. Expected outcomes include a versatile inhala ....A virtual platform for animal–human inhalation toxicity extrapolation. This project aims to remove the long-lasting barrier in extrapolating data from animals to humans by developing an integrated virtual platform. This project expects to fully resolve inhalation exposure differences in nasal airways between commonly used animal surrogates and humans, which could lay scientific underpinnings in developing rigorous interspecies data conversion schemes. Expected outcomes include a versatile inhalation exposure risk assessment tool that can be implemented for any airway compartment, enhanced reliability of animal tests, reduced number of animals for testing. This should provide significant benefits in improving occupational health and safety and promoting National/International regulatory changes. Read moreRead less
Building insights of our largest terrestrial carbon sink: rangelands soils. Rangelands soils represent Australia’s largest carbon sink. Yet, little is known about their potential for carbon sequestration or their vulnerability to climate and environmental change. This project leverages investments in national terrestrial observation platforms and integrates previous research outputs to develop new methods to measure and build understanding of soil carbon composition and dynamics in rangeland eco ....Building insights of our largest terrestrial carbon sink: rangelands soils. Rangelands soils represent Australia’s largest carbon sink. Yet, little is known about their potential for carbon sequestration or their vulnerability to climate and environmental change. This project leverages investments in national terrestrial observation platforms and integrates previous research outputs to develop new methods to measure and build understanding of soil carbon composition and dynamics in rangeland ecosystems. Under a framework that connects detailed measurements and small-scale processes, with machine-learning, data-model assimilation and large-scale next-generation biogeochemical modelling, it’ll allow more accurate predictions of soil carbon change and better decision-making to guide sustainable rangelands management.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC180100030
Funder
Australian Research Council
Funding Amount
$3,925,357.00
Summary
ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching ....ARC Training Centre for Transforming Maintenance through Data Science. The ARC Training Centre for Transforming Maintenance through Data Science aims to equip practising engineers and Australian graduates with the next generation of data science methods for the maintenance sector. The Centre plans to introduce timely and cost-efficient maintenance scheduling by developing data-intensive mathematical and computational algorithms for asset management and fault prediction. The Centre’s overarching objectives are to enable development and adoption of new practices to improve productivity and asset reliability for industry and to foster a new maintenance technology service sector for national and international markets.Read moreRead less
Self-organised communication as a foundation of large, complex societies. This Project aims to investigate how evolution has shaped the self-organisation of robust communication networks that emerge in large animal collectives from the actions of individuals following only simple, local rules. It expects to generate new knowledge into the fundamental principles guiding the self-organisation of networks that can sustain a complex society. Empirical work with ant colonies will inform the construct ....Self-organised communication as a foundation of large, complex societies. This Project aims to investigate how evolution has shaped the self-organisation of robust communication networks that emerge in large animal collectives from the actions of individuals following only simple, local rules. It expects to generate new knowledge into the fundamental principles guiding the self-organisation of networks that can sustain a complex society. Empirical work with ant colonies will inform the construction of simulation models to push the investigation beyond experimental limits. The Project should significantly advance our understanding of how communication networks enable the development of large societies, and thus of how to better manage autonomous man-made networks, most importantly the Internet-of-Things.Read moreRead less