93 computer-programmer-"Prof"-"Prof" Postdoctoral positions at University of Oxford in United Kingdom
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will have or be close to the completion of a PhD in Neuroscience, Psychology or a closely related discipline. With in-depth knowledge of cognitive and computational neuroscience including motivation
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. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute for Health Research (NIHR) and provides highly rated medical
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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well as access to the group dedicated computing cluster environment with H100, L40s, and A40 GPUs. This post is funded by the UKRI Future Leaders Fellowship, a flexible long-term public funding scheme
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About the role The Kelly lab is excited to announce a new post-doctoral position in computational biology. This position is funded as part of an international consortium of scientists who
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research programmes in battery modelling. Preference will be given to applicants with prior experience of optimisation, PyBaMM and/or electrochemistry. We proudly hold a departmental Athena SWAN Silver Award
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about Cancer Immunology, T cell Biology and translational research and will have good knowledge of these subjects to contribute to established research programmes. Excellent project management abilities
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, operations research, computer science, mathematical finance, or a related field, the successful candidate will demonstrate the ability to develop independent research ideas and contribute to advancing our
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Engineering, Mathematics, Statistics, Computer Science or conjugate subject; strong record of publication in the relevant literature; good knowledge of machine learning algorithms and/or statistical methods
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collaboration with the Translational Gastroenterology Unit (TGU) and the Ludwig Institute of Cancer Research (LICR) we aim to develop a computer guided endoscopy image recognition system that will support