47 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions at University of Oxford
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learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to [email protected] For more information about working at the Department
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We are seeking a full-time Postdoctoral Research Assistant in Machine Learning to join Torr Vision Group at the Department of Engineering Science (central Oxford). The post is fixed-term for 1 year
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to [email protected] For more information about working at
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for flexibility in the start and end dates, subject to approval by the department and the funding agencies. The successful candidate will join the Machine Learning & Data Science research group and conduct research
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About the role Applications are invited for a Postdoctoral Research Associate in Artificial Molecular Machines to work under the supervision of Professor Matthew Langton for a period of up to 24
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funded by UKRI EPSRC, is fixed term for up to 12 months maximum, and must end by 31/12/2026. You will be contributing to joint UKRI EPSRC – NSF CBET project on sustainable computer networks, with a focus
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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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near completion of) in Machine Learning or Maths. Informal enquiries may be addressed to [email protected] For more information about working at the Department, see www.eng.ox.ac.uk/about/work
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/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
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substantial experience of machine learning in a research or industry environment, and have the ability and willingness to combine machine learning research with sustained engagement with historical and