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projects in computer vision research, with a particular emphasis on Spatial Intelligence, 3D Computer Vision, and 3D Generative AI. You should hold a relevant PhD/DPhil (or near completion*) in Computer
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The Oxford-GSK Institute of Molecular & Computational Medicine has a new and exciting opportunity for a Postdoctoral Researcher, specialising in Neuronal Dysfunction in Alzheimer’s Disease, to join
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experimentally investigated. About you You should possess a PhD or DPhil (or be near completion of) in the field of engineering, physics or applied mathematics together with relevant experience in the field
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inference attacks, to mitigate privacy leaks in MMFM. You will hold a PhD/DPhil (or be near completion) in a relevant discipline such as computer science, data science, statistics or mathematics; expertise in
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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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. To address these questions, we combine a series of interdisciplinary approaches ranging from experimental embryology and fluorescent microscopy to mathematical modelling. The lab is highly interdisciplinary
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-certification, and redeployment, as well as social acceptability and policy design. About you You should hold a relevant PhD/DPhil, or be near completion, in electrical engineering, economics, applied mathematics
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of agentic behaviour and publishing high-impact research. Candidates should possess a PhD (or be near completion) in PhD in Computer Science, AI, Security, or a related field. You will have a Strong background
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support a high-value materials characterisation programme as a postdoctoral researcher. The ability to think outside the box with creativity, along with having the drive and ambition to develop those ideas
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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