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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | about 1 month ago
safeguard Australia’s future. At the heart of ACCESS is a community of Research Software Engineers with a range of skills including high-performance computing and data (HPCD), climate and Earth system model
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involve a lot of experiments using DL/AI methods. We will use the Monash <b>High</b> <b>Performance</b> <b>Computing</b> platform (MASSIVE) to do the experiments. Required knowledge You must have experience in Deep Learning, and have strong programming skills. Learn more about minimum entry...
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Appointment Type : Full-time, continuing Location: Macquarie University, Wallumattagal Campus, North Ryde Macquarie University is seeking an outstanding academic to join the School of Computing in the
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, powertrain systems, aerodynamics, lightweight structures, and high‑performance materials aligned to race and road vehicle development, alongside advanced manufacturing processes relevant to both
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should be loaded on to which vehicle and deciding which package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to
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should be loaded on to which vehicle and deciding which package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to
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and reputation of the School of Computing, Faculty of Science and Engineering, and Macquarie University. About us You will be joining a growing School of Computing in the Faculty of Science and
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Convolutional Neural Networks", ICML 2021 - "Privacy-Preserving Feature Selection with Secure Multiparty Computation", ICML 2021 - "High Performance Logistic Regression for Privacy-Preserving Genome
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Convolutional Neural Networks", ICML 2021 - "Privacy-Preserving Feature Selection with Secure Multiparty Computation", ICML 2021 - "High Performance Logistic Regression for Privacy-Preserving Genome
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introduces quantum-inspired representations and optimisation techniques to model student engagement, performance trajectories, and behavioural uncertainty more effectively, enabling more accurate