154 phd-in-theoretical-neuroscience Postdoctoral positions at University of Oxford in Uk
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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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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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research outcomes to advance knowledge in the specialist area. It is essential that you hold a PhD/DPhil (or close to completion) in biochemistry, molecular biology, neuroscience or a related field, and have
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of the research group, including postdocs, research assistants, technicians, PhD and project students, as well as to represent the PI and ISML when required. You will hold a Masters or PhD degree (or be close to
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Scripta Therapeutics to develop this knowledge into a screen to identify novel therapeutics. About You • Have, or be close to the completion of PhD/DPhil, in molecular or cell biology or
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the Courtroom: Trauma-informed Specialist Courts and the Medicalisation of Justice’. This five-year, interdisciplinary project explores the role of trauma informed care and neuroscience in transforming trial
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screening (XChem), PDB deposition and biophysical techniques including SPR, DSF and NMR. Applicants must hold a PhD in Biochemistry/ Biophysics / Chemical Crystallography or a related field (or have submitted
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facilely applied as a photoluminescent layer, targeting in situ detection and non-contact visualization of surface temperatures and pressures. You should hold a PhD/DPhil (or near completion*) in materials
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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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and evaluation. The post holder will take a leading role in advancing theoretical and algorithmic research in the domain of probabilistic preference aggregation, contribute to the design and analysis