Algal direct-air CO2 capture through interfacial enzyme immobilisation . Capturing CO2 directly from the atmosphere is challenging due to inherently slow mass transfer kinetics. This project aims to overcome this using an enzyme that can rapidly solubilise CO2 from air into water, to produce algae. By engineering the enzyme immobilisation at the air-water interface, this project will activate and protect the enzymes, increasing their lifespan and reducing costs. By understanding mass transfer an ....Algal direct-air CO2 capture through interfacial enzyme immobilisation . Capturing CO2 directly from the atmosphere is challenging due to inherently slow mass transfer kinetics. This project aims to overcome this using an enzyme that can rapidly solubilise CO2 from air into water, to produce algae. By engineering the enzyme immobilisation at the air-water interface, this project will activate and protect the enzymes, increasing their lifespan and reducing costs. By understanding mass transfer and enzyme activity in the interfacial immobilisation media, floating enzyme rafts can be developed for deployment over expansive areas, facilitating large-scale conversion of atmospheric CO2 into algae-derived fuels, feeds and chemicals.Read moreRead less
New biocatalysts for selective chemical oxidations under extreme conditions. This project will identify and design new enzyme biocatalysts which function under extreme conditions such as elevated temperature and high concentrations of peroxides. These enzymes will be sourced from microorganisms which are located in extreme biological environments e.g. hot springs (the so-called extremophiles). The expected outcome of this project are the identification of robust enzymes which can catalyse select ....New biocatalysts for selective chemical oxidations under extreme conditions. This project will identify and design new enzyme biocatalysts which function under extreme conditions such as elevated temperature and high concentrations of peroxides. These enzymes will be sourced from microorganisms which are located in extreme biological environments e.g. hot springs (the so-called extremophiles). The expected outcome of this project are the identification of robust enzymes which can catalyse selective oxidation reactions in complex organic molecules, such as steroids. The new biocatalysts developed in this project will have significant benefit in the development of new routes to access bespoke molecules of value in fine chemical synthesis and drug development.
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Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising ap ....Searching for near-exact protein models. This project aims to develop novel and efficient heuristic-based algorithms leading to near accurate protein tertiary structure models. Knowledge about protein structures is fundamental to our understanding of living systems. The progress on experimental determination of these structures has been extremely limited and remains an open challenge in molecular biology. Computational prediction of protein structures from sequences is emerging as a promising approach, but its accuracy is far from satisfactory. The software systems developed in this project will be used in structural identification of target proteins in drug design. This will make drug design process more efficient, saving time and cost, potentially saving lives.Read moreRead less
Nano-reactors: Protein cages as reusable scaffolds for designer enzymes. This project aims to develop robust protein cages derived from the coats of viruses to contain heat-stable P450 enzymes, for use as specialised protein bio-catalysts in chemical industries. A valuable chemical precursor of renewable bio-plastics will be produced from seed oils by enzymes, reducing the use of fossil fuels. This synthetic biology approach combines biotechnology, nanotechnology and protein engineering to estab ....Nano-reactors: Protein cages as reusable scaffolds for designer enzymes. This project aims to develop robust protein cages derived from the coats of viruses to contain heat-stable P450 enzymes, for use as specialised protein bio-catalysts in chemical industries. A valuable chemical precursor of renewable bio-plastics will be produced from seed oils by enzymes, reducing the use of fossil fuels. This synthetic biology approach combines biotechnology, nanotechnology and protein engineering to establish a plant-based platform biotechnology for using enzymes as catalysts to make high-value molecules. The project aims to show how to engineer clean, sustainable chemistry in designer nano-environments. This should make synthetic processes more sustainable and enhance advanced chemical manufacturing in Australia.Read moreRead less
Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predi ....Low-cost Sensing Methods and Hybrid Learning Models. This project aims to revolutionise the theory and practice of sensing and monitoring by developing novel Artificial Intelligence and Internet of Things technologies. This project expects to generate new knowledge in the area of Artificial Intelligence of Things by combining sensing, machine learning, and big data analytics. Expected outcomes of this project include novel low-cost sensing methods and new hybrid machine learning models for predictive sensory data analytics. This should provide significant benefits, such as substantially reduced operating and service costs and improved accuracy for real-time monitoring in the fields where cheap-to-implement and easy-to-service monitoring systems over large geographical areas are imperative.Read moreRead less
Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries. Partially Observable Markov Decision Processes (POMDPs) provide a general mathematical framework for sequential decision making under uncertainty. However, solving POMDPs effectively under realistic assumptions remains a challenging problem. This project aims to develop new efficient Monte Carlo algorithms to significantly advance the application of POMDPs to real-world decision problems involving complex action spaces an ....Partially Observable MDPs, Monte Carlo Methods, and Sustainable Fisheries. Partially Observable Markov Decision Processes (POMDPs) provide a general mathematical framework for sequential decision making under uncertainty. However, solving POMDPs effectively under realistic assumptions remains a challenging problem. This project aims to develop new efficient Monte Carlo algorithms to significantly advance the application of POMDPs to real-world decision problems involving complex action spaces and system dynamics. Both theoretical and algorithmic approaches will be applied to sustainable fishery management --- an important problem for Australia and an ideal context for POMDPs. The project will advance research in artificial intelligence, dynamical systems, and fishery operations, and benefit the national economy.Read moreRead less
Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global com ....Two-way Auslan: Automatic Machine Translation of Australian Sign Language. This project aims to develop an automatic two-way machine-translation system between Auslan (Australian Sign Language) and English by researching and leveraging advanced computer vision and machine learning technology. The project expects to advance research in AI technology on topics including visual recognition, language processing and deep learning. This will boost Australia's national research capacity and global competitiveness. Expected outcomes of this project will help to break the communication barriers between the Deaf and hearing population. This should provide significant benefits to Deaf communities through enhanced communication and improved quality-of-life, leading to a fair, more inclusive and resilient Australian society.Read moreRead less
Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection ....Deep Learning Architecture with Context Adaptive Features for Image Parsing. This project aims to develop a novel deep learning network architecture with contextual adaptive features for image parsing that can improve the object detection accuracy in real-world applications. A number of innovative methods for deep learning, contextual features and network parameter selection will be developed and investigated. The impact of the proposed architecture and features will be improved object-detection accuracy and advances in deep learning network architecture for image parsing. The intended outcomes are deep learning network architecture, contextual feature extraction techniques and network parameter optimisation techniques for image parsing.Read moreRead less
Linking human brain structure to function with ultra-high resolution fMRI. This project will examine the structure and function of the sensory cortex of the human brain using ultra-high resolution functional magnetic resonance imaging (7 Tesla MRI). The project pushes new boundaries for resolution with ultra-high field MRI (7 Tesla) and, as such, will advance techniques for the acquisition, analysis, and computational modelling of high-resolution fMRI brain imaging, providing detail of the funct ....Linking human brain structure to function with ultra-high resolution fMRI. This project will examine the structure and function of the sensory cortex of the human brain using ultra-high resolution functional magnetic resonance imaging (7 Tesla MRI). The project pushes new boundaries for resolution with ultra-high field MRI (7 Tesla) and, as such, will advance techniques for the acquisition, analysis, and computational modelling of high-resolution fMRI brain imaging, providing detail of the functional organisation of the sensory cortex at a level never previously possible in the living human brain. This will provide new understanding of the neural-level networks that underpin attention and touch perception in the human brain.Read moreRead less
A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extrac ....A Novel Automatic Neural Network Feature Extractor. This project aims to study feature extraction abilities of convolutional as well as traditional neural networks and develop a generic feature extractor which can be applied to wide variety of real-world image and non-image data. New concepts for automatic feature extraction, feature explanation, hybrid evolutionary algorithms and non-iterative ensemble learning will be introduced and evaluated. The expected outcomes are a generic feature extractor for automatically extracting features, an optimiser for finding optimal parameters and non-iterative ensemble learning technique for classification of features into classes. The impact of this project will be automatic feature extractors and classifiers for real-world applications.Read moreRead less