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detection models, with a focus on achieving generalisable multimodal understanding in zero-shot settings. About You The successful candidate must have a PhD (or equivalent) in the field of computer vision or
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London, with a team of investigators covering AI, computer vision, robotics, and medical imaging. You will join a dynamic and successful team with access to both cutting-edge computer power and advanced
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Development for more information. About you: To be successful in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD (awarded or near completion) in a
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skills and experience: Essential criteria PhD (awarded or near completion) in a relevant discipline, such as psychology or neuroscience, with a strong interest in neurodevelopmental science. Knowledge
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Oxford’s Department of Orthopaedics (NDORMS) as well as collaborators in Bristol and Cardiff. You should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely
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engineering. We aim to unravel the logic of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse
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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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for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling
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Vision, Robotics, Evolutionary Computation, Deep Reinforcement Learning, and Machine Learning. This should include a proven publication track record. You should also have: Research Associate: A PhD (or
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of genome organisation and metabolic control—with the bold vision of building synthetic life. In this role, you will develop and apply computational methods to analyse single-cell modalities, focusing on gene