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global leader in dual-sector learning and research by 2028. Join us on the journey and help us achieve our strategic drives embedded in our Strategic Plan 2022-2028: Start well, finish brilliantly. One
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experience in at least one of the following areas: deep learning, active learning, deep reinforcement learning, and natural language processing.
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one or more of the following areas: complex quantum processes, quantum error corrections, tensor networks, optimisation and machine learning, and developing software infrastructure Some experience in
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units and w's represent the weights of the neural network. References: [1] Buser Say, Ga Wu, Yu Qing Zhou and Scott Sanner. Nonlinear hybrid planning with deep net learned transition models and mixed
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degree by research has a 3-year deadline during which many students in the biological sciences struggle to learn the varied new techniques proficiently in a timely manner. Far too many students spend 6 to
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levels for manufacturing, routing delivery trucks for transport, scheduling power stations and electricity grids, to name just a few. In recent years, deep learning is showing startling ability
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the shortcomings of these techniques, deep learning is more and more involved in static vulnerability localization and improving fuzzing efficiency. This project aims to deliver a smart software vulnerability
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‘dynamic graphs’. Although recently many studies on extending deep learning approaches for graph data have emerged, there is still a research gap on extending deep learning approaches for identifying
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. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
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Project description: On behalf of the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), we will establish the role of artificial intelligence (AI) deep learning to improve the prediction