Mid-Career Industry Fellowships - Grant ID: IM230100090
Funder
Australian Research Council
Funding Amount
$1,053,046.00
Summary
Multi material 3D Printing. This project aims to further develop a new 3D printing technique commercialised by an Australian start-up company. Current electronics manufacturing is extremely capital intensive, slow and restrictive in 3D design. The 3D printing method proposed in this application will disrupt the current advanced manufacturing eco system; creating unique methods to unlock advances in diverse markets for example, photovoltaics, printed circuit boards and sensors. The expected outco ....Multi material 3D Printing. This project aims to further develop a new 3D printing technique commercialised by an Australian start-up company. Current electronics manufacturing is extremely capital intensive, slow and restrictive in 3D design. The 3D printing method proposed in this application will disrupt the current advanced manufacturing eco system; creating unique methods to unlock advances in diverse markets for example, photovoltaics, printed circuit boards and sensors. The expected outcomes of this project are to create new commercial opportunities for the next generation of 3D printed electronics. This will provide significant benefits, creating unique capability to manufacture devices in 3D - faster, cheaper and with reduced reliance on global supply chains.Read moreRead less
Novel water treatment processes. The objective of this project is the discovery of novel methods for the treatment and reuse of water for both industrial and household applications. Improved treatment systems with the potential for water reuse offer significant improvements to our overall water management potential. The first part of the project is designed to focus on the study of hot bubble column evaporators for solute decomposition, sterilisation and the de-watering of heavily contaminated i ....Novel water treatment processes. The objective of this project is the discovery of novel methods for the treatment and reuse of water for both industrial and household applications. Improved treatment systems with the potential for water reuse offer significant improvements to our overall water management potential. The first part of the project is designed to focus on the study of hot bubble column evaporators for solute decomposition, sterilisation and the de-watering of heavily contaminated industrial wastewater. The second part would be based on the study of a suitable depth filter medium for the treatment of partially treated household sewage water. This is designed to form part of an on-site household sewage water treatment and reuse system which is currently being developed.Read moreRead less
3D Vision Geometric Optimisation in Deep Learning. This project aims to develop a methodology for integrating the algorithms of 3D Vision Geometry and Optimization into the framework of Machine Learning and demonstrate the wide applicability of the new methods on a variety of challenging fundamental problems in Computer Vision. These include 3D geometric scene understanding, and estimation and prediction of human 2D/3D pose and activity. Applications of this technology are to be found in Intell ....3D Vision Geometric Optimisation in Deep Learning. This project aims to develop a methodology for integrating the algorithms of 3D Vision Geometry and Optimization into the framework of Machine Learning and demonstrate the wide applicability of the new methods on a variety of challenging fundamental problems in Computer Vision. These include 3D geometric scene understanding, and estimation and prediction of human 2D/3D pose and activity. Applications of this technology are to be found in Intelligent Transportation, Environment Monitoring, and Augmented Reality, applicable in smart-city planning and medical applications such as computer-enhanced surgery. The goal is to build Australia's competitive advantage in the forefront of ICT research and technology innovation.Read moreRead less
Support needs assessment: a developmental model for use in support, training and funding for individuals with single and multiple disabilities. This research project aims to construct and trial a new model for the assessment of the support needs of persons with disabilities. It addresses a number of critical deficiencies which recent research has shown to undermine the reliability, validity and usefulness of existing support needs instruments. Specifically, it is proposed to collect data that ....Support needs assessment: a developmental model for use in support, training and funding for individuals with single and multiple disabilities. This research project aims to construct and trial a new model for the assessment of the support needs of persons with disabilities. It addresses a number of critical deficiencies which recent research has shown to undermine the reliability, validity and usefulness of existing support needs instruments. Specifically, it is proposed to collect data that will inform the determination of the support needs of people with different kinds and combinations of disabilities. Variations in maintenance and intervention supports over time and context will be considered. The assessment system planned will have the flexibility to meet a number of potential user objectives.Read moreRead less
Mineral content of leaves and the ratio of water loss to carbon gain: environmental and genetic controls and comparison with stable isotopic measures. The ash content of leaves has promise as a cheap screen of water-use efficiency or of 'vigour' in crop plants, but the underlying mechanisms are not understood. The underlying science is at the intersection of plant growth, water use and nutrition. This project will aid breeders in understanding the conditions under which the screen may work.
Discovery Early Career Researcher Award - Grant ID: DE140101143
Funder
Australian Research Council
Funding Amount
$395,220.00
Summary
An electrophysiological insight into the role of chloroplasts in stomatal drought signalling. Drought implies a range of stresses with which plants have to cope. Drought is not only a domestic issue for Australian people who live in this dry continent but also significantly affects global food supply and drives climate change. Stomata guard cells exert major controls on global water and carbon cycles. Although the total stomatal pore area may be five per cent of a leaf surface, transpirational w ....An electrophysiological insight into the role of chloroplasts in stomatal drought signalling. Drought implies a range of stresses with which plants have to cope. Drought is not only a domestic issue for Australian people who live in this dry continent but also significantly affects global food supply and drives climate change. Stomata guard cells exert major controls on global water and carbon cycles. Although the total stomatal pore area may be five per cent of a leaf surface, transpirational water loss through the stomata contributes to 70 per cent of total agricultural water usage. As an environmental signal, drought regulates stomatal movements. This project seeks to understand the mechanisms of drought induced molecular retrograde signals and their regulation over stomata. The outcomes will aid the development of strategies for reducing water loss from crops.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE180100628
Funder
Australian Research Council
Funding Amount
$368,446.00
Summary
Machine vision techniques for solar power forecasting and generation. This project aims to advance the research in short-term solar power forecasting and optimise the generation process using machine vision techniques. This project will use cameras to capture images of sky and mirror surfaces of heliostats. The scientific novelties are the exploration of geometry-aware feature representations for solar power prediction and building three-dimensional models of mirror surfaces of heliostats to opt ....Machine vision techniques for solar power forecasting and generation. This project aims to advance the research in short-term solar power forecasting and optimise the generation process using machine vision techniques. This project will use cameras to capture images of sky and mirror surfaces of heliostats. The scientific novelties are the exploration of geometry-aware feature representations for solar power prediction and building three-dimensional models of mirror surfaces of heliostats to optimise the solar power generation process. The outcome is a working prototype to boost the solar power forecasting accuracy and a three-dimensional reconstruction system to be helpful for the solar power generation. These outcomes will highly benefit the short-term solar power forecasting, generation and electricity grid management systems.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL190100056
Funder
Australian Research Council
Funding Amount
$2,795,000.00
Summary
Smart Plants and Solutions for Enhancing Crop Resilience and Yield. The Fellowship aims to produce transformative solutions targeting crop resilience and food security. The chloroplast, the site of photosynthesis, regulates a suite of cellular processes that control photosynthesis, growth and drought resilience. It is expected that a first ever blueprint of the suite of communication networks used by the chloroplast will be discovered. I will use synthetic biology to rewire the network in order ....Smart Plants and Solutions for Enhancing Crop Resilience and Yield. The Fellowship aims to produce transformative solutions targeting crop resilience and food security. The chloroplast, the site of photosynthesis, regulates a suite of cellular processes that control photosynthesis, growth and drought resilience. It is expected that a first ever blueprint of the suite of communication networks used by the chloroplast will be discovered. I will use synthetic biology to rewire the network in order to generate 'smart plants' that are higher-yielding and more resilient in both good and bad seasons by precisely switching on and off resilience. Such re-imaginings of crop systems, inclusive of societal implications, will help chart the future of Australian agriculture.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE220101520
Funder
Australian Research Council
Funding Amount
$439,700.00
Summary
A New Era of Galactic Archaeology with Large Surveys and Machine Learning. The project aims to advance the symbiotic relation between astronomy and machine learning to unravel the origin and the evolutionary history of the Milky Way. The proposed study will base heavily on the data from the Australian-led spectroscopic survey and, as a result, contribute to realising the full potential of this multi-million dollar endeavour. The goal of the study is to walk ourselves back in cosmic time, using t ....A New Era of Galactic Archaeology with Large Surveys and Machine Learning. The project aims to advance the symbiotic relation between astronomy and machine learning to unravel the origin and the evolutionary history of the Milky Way. The proposed study will base heavily on the data from the Australian-led spectroscopic survey and, as a result, contribute to realising the full potential of this multi-million dollar endeavour. The goal of the study is to walk ourselves back in cosmic time, using the most advanced technologies of our time to reveal the Milky Ways oldest story. The investigation aims to consolidate Australia's position in big data astronomy and give Australia a unique competitive advantage in data analytics. Such an endeavour is essential for Australia to maintain its leadership in astronomy.Read moreRead less
Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between
neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this pr ....Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between
neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications.
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