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
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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Fractional dynamic models for MRI to probe tissue microstructure. This project aims to develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. In magnetic resonance imaging, mathematical models and their parameters play a key role in associating information between images and biology, with the overall aim of producing spatially resolved maps of tissue property variations. However, models which can inform on changes in mi ....Fractional dynamic models for MRI to probe tissue microstructure. This project aims to develop new mathematical tools for mapping tissue microstructural properties via the use of space-time fractional calculus methods. In magnetic resonance imaging, mathematical models and their parameters play a key role in associating information between images and biology, with the overall aim of producing spatially resolved maps of tissue property variations. However, models which can inform on changes in microscale tissue properties are lacking. The tools developed by this project will be used to generate new magnetic resonance image based maps to convey information on tissue microstructure changes in the human brain. Additionally, the mathematical tools developed will be transferable to other applications where diffusion and transport in heterogeneous porous media play a role.Read moreRead less
Advanced materials for space propulsion: satellites and cubesats. Poorly controlled interactions between plasmas and surfaces often mean loss of process efficiency and surface degradation over time. For Hall thrusters, a type of engine used to move satellites in space, this means increased fuel consumption and shorter useful life. Through modelling and experiment, this project will show how intelligent selection of advanced materials and plasma parameters can minimise surface wear, enable in sit ....Advanced materials for space propulsion: satellites and cubesats. Poorly controlled interactions between plasmas and surfaces often mean loss of process efficiency and surface degradation over time. For Hall thrusters, a type of engine used to move satellites in space, this means increased fuel consumption and shorter useful life. Through modelling and experiment, this project will show how intelligent selection of advanced materials and plasma parameters can minimise surface wear, enable in situ material repair to extend device lifetime, and modulate plasma properties to increase thruster efficiency for a given task. These benefits enable reliable propulsion platforms for massive communication and observation satellite networks and deep space exploration.Read moreRead less
Industrial Transformation Research Hubs - Grant ID: IH210100051
Funder
Australian Research Council
Funding Amount
$5,000,000.00
Summary
The ARC Research Hub for Digital Bioprocess Development. The ARC Hub for Digital Bioprocess Development aims to assist the Biopharma industry by increasing digital innovation, productivity and competitiveness. An interdisciplinary team of engineers, scientists and computing specialists will develop digitally integrated advanced manufacturing processes and a platform for industry adoption. The program will address key bioprocessing research challenges and develop new process and digital models th ....The ARC Research Hub for Digital Bioprocess Development. The ARC Hub for Digital Bioprocess Development aims to assist the Biopharma industry by increasing digital innovation, productivity and competitiveness. An interdisciplinary team of engineers, scientists and computing specialists will develop digitally integrated advanced manufacturing processes and a platform for industry adoption. The program will address key bioprocessing research challenges and develop new process and digital models that can predict and optimise manufacturing processes, resulting in greater yields, faster and more flexible processes and enhanced product stability. The Hub will transform biopharmaceutical manufacturing and unlock growth opportunities to forge an internationally competitive Australian Biopharma sector.Read moreRead less
CropVision: A next-generation system for predicting crop production. Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with down side risk caused by climate extremes and market volatilities. However, current systems for predicting crop production are inaccurate and unreliable. This project aims to develop a next generation system for advance and high accuracy predictions for yield, crop type and area at field scale. This will be don ....CropVision: A next-generation system for predicting crop production. Accurate and timely production estimates are essential to Australia’s grain producers and industry to better deal with down side risk caused by climate extremes and market volatilities. However, current systems for predicting crop production are inaccurate and unreliable. This project aims to develop a next generation system for advance and high accuracy predictions for yield, crop type and area at field scale. This will be done by integrating the state of the art global climate models (GCM), biophysical crop modelling, and high-resolution earth observation technologies. This project will deliver a next generation crop prediction system to predict crop production at field scale for improved decision-making and enhancing resilience.Read moreRead less
Reducing direct greenhouse gas emissions from urban wastewater systems. This project aims to develop a systematic framework for water utilities to monitor and reduce direct greenhouse gas (GHG) emissions from wastewater systems. A standardised monitoring protocol will be developed to conduct an unprecedented nationwide sampling campaign. The obtained data, with microbial characterisation and mechanism analysis, will be used to develop novel models for accurate prediction of GHG emissions. Expect ....Reducing direct greenhouse gas emissions from urban wastewater systems. This project aims to develop a systematic framework for water utilities to monitor and reduce direct greenhouse gas (GHG) emissions from wastewater systems. A standardised monitoring protocol will be developed to conduct an unprecedented nationwide sampling campaign. The obtained data, with microbial characterisation and mechanism analysis, will be used to develop novel models for accurate prediction of GHG emissions. Expected outcomes include protocol to accurately monitor emissions, models to predict emission under various conditions, and mitigation guideline for typical plant configurations. The anticipated benefit is a significant reduction in GHG emissions from urban water industry and support it to meet net-zero-emission goal by 2050.Read moreRead less
Early Career Industry Fellowships - Grant ID: IE230100422
Funder
Australian Research Council
Funding Amount
$386,637.00
Summary
Feasible quantification of greenhouse gas emitted from wastewater treatment. This project aims to develop an accurate and practical approach to quantify greenhouse gas (GHG) emissions from wastewater treatment. Australian water utilities have pledged to net-zero emissions. However, most utilities do not know its actual emissions due to lack of feasible quantification method. This project will apply an interdisciplinary approach via mechanism investigations, mathematical modelling, and field work ....Feasible quantification of greenhouse gas emitted from wastewater treatment. This project aims to develop an accurate and practical approach to quantify greenhouse gas (GHG) emissions from wastewater treatment. Australian water utilities have pledged to net-zero emissions. However, most utilities do not know its actual emissions due to lack of feasible quantification method. This project will apply an interdisciplinary approach via mechanism investigations, mathematical modelling, and field works to develop and validate a new feasible quantification method. This project will also advance knowledge on GHG emissions to guide quantification design. The outcomes will be translated into industry protocols and disseminated into industry. The outcomes provide timely support to water sector on its pathway to net-zero.Read moreRead less
Augmented Sociality: Enabling a Socialised Experience of Augmented Reality. This project will explore new socialised uses of Augmented Reality (AR) that expand creativity, social relations, and participation. We seek to better understand how AR content can be leveraged by people to create their own new ways of learning, collaborating, and relating with each other. To do so we will study and prototype new tools and platforms to allow non-experts to create their own AR media. We aim to enable peop ....Augmented Sociality: Enabling a Socialised Experience of Augmented Reality. This project will explore new socialised uses of Augmented Reality (AR) that expand creativity, social relations, and participation. We seek to better understand how AR content can be leveraged by people to create their own new ways of learning, collaborating, and relating with each other. To do so we will study and prototype new tools and platforms to allow non-experts to create their own AR media. We aim to enable people of all ages, education, and background, to imagine and create, and not just passively consume, AR contents, services, and applications. We will generate new applications of AR, a new platform to collaboratively create these applications, and a new theory of 'Augmented Sociality' to guide AR design.
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