72 machine-learning "https:" "https:" "https:" "https:" "The Open University" Postdoctoral positions at University of Oxford
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to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
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* together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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Oxford Population Health (the Nuffield Department of Population Health) provides an excellent environment for multi-disciplinary research and teaching and for professional and support staff. We work together to answer some of the most important questions about the causes, prevention and...
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We are seeking to appoint a Senior Postdoctoral Researcher in Statistical Machine Learning and Deep Generative Modelling to apply and develop cutting-edge deep generative probabilistic models
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the next generation of PV technologies for beyond 2030. The new postdoctoral research position will use materials modelling techniques (DFT, molecular dynamics, machine learning potentials) to investigate
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Vision or Machine Learning. You should have a strong publication record at the principal international computer vision and machine learning conferences and should hold sufficient theoretical and practical
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About the role We are seeking a full-time Postdoctoral Researcher to join the Oxford Secure and Advanced Computer Architecture Research (OSCAR) group at the Department of Engineering Science
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collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
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will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine
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will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical