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an advanced AI-augmented digital platform (AiCT-Med) powered by cutting edge machine learning models trained on multiple large, aged care datasets from providers across Australia. The platform is designed
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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to engage with multidisciplinary teams and external partners. Desirable attributes include experience with spatio-temporal models, machine learning, Bayesian methods, and knowledge of environmental exposure
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
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and manufacturing, flight mechanics and dynamics, propulsion, and/or aerospace numerical methods including machine learning, in a university of similar standing to the University of Sydney
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm
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engineering, aerospace design and manufacturing, flight mechanics and dynamics, propulsion, and/or aerospace numerical methods including machine learning, in a university of similar standing to the University
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engineering, aerospace design and manufacturing, flight mechanics and dynamics, propulsion, and/or aerospace numerical methods including machine learning, in a university of similar standing to the University
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numerical methods including machine learning, in a university of similar standing to the University of Sydney. The successful candidate must have a proven ability to manage units of study with large to very
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numerical methods including machine learning, in a university of similar standing to the University of Sydney. The successful candidate must have a proven ability to manage units of study with large to very