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learning at scale. Research directions include designing algorithms and methods for adaptive and personalised feedback, modelling learning behaviours with sequence and deep learning methods, and generating
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-growing range of relational geochemistry data models, the University of Sydney EarthBank Project node will focus on the creation of innovative platform functionalities and numerical workflows to facilitate
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, paleoecology, paleoclimatology, numerical modelling, relational database design, and geological data science a strong track record of publications in leading international scientific journals (Q1) and effective
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expertise in at least one of the following: geochemistry, carbonate sedimentology, paleoecology, paleoclimatology, numerical modelling, relational database design, and geological data science a strong track
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at the University of Sydney has an international reputation in the field of turbulent combustion, combustion modelling and advanced laser diagnostics. Specific research issues include the combustion of bio-fuels
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for everyone. Our research excellence is recognised by numerous prizes and awards to individual staff members and the award of over $7 million in research funding each year. Our staff are also recognised
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-based algorithms (e.g., GNNs, deep reinforcement learning) design and simulate dynamic models of megaproject systems prepare and submit journal articles to high-impact publications contribute
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) in FPGA design, machine learning or a related field experience in the development of machine learning models using Python and pytorch expertise in two or more of the following technical areas: design