32 post-doc-fellowship-computer-vision Postdoctoral positions at University of London
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View All Vacancies Department of Drama, Theatre and Dance Location Egham Salary £40,839 to £48,003 per annum pro rata - including London Allowance Post Type Part Time Hours per Week 10.5 Weeks per
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the diversity of the astrophysics community and particularly encourage applications from underrepresented groups. About the School/Department/Institute/Project This position is part of the Royal Society
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qualification/experience equivalent to PhD level in a relevant subject area (physics, engineering, computing science, etc.). You will need as essential skills a good knowledge of C++ and python, familiarity with
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desirable if the post holder has experience in the development of production-quality software, computational Bayesian inference, and strong communication skills. For more information see the detailed job
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About the Role We are looking for a Postdoctoral Research Assistant to work with Dr Chema Martin on a Human Frontiers Science Program Research Grant project entitled “Evolutionary Biophysics
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motivated computational Postdoctoral Research Assistant to lead on an established and successful research line aimed at understanding the genetic events that drive cancer evolution. We have a long-lasting
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developmental science. The successful candidate will contribute to a major research programme investigating how educational experiences shape mental health from childhood into adulthood. The role involves working
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About the Role A 12 month post-doctoral research assistant position funded by the Barts and the London Charity (BTLC) is available in the laboratory of the laboratory of Professor Stuart McDonald
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of London. This Welcome Trust Funded post will be based at the Centre for Molecular Cell Biology (CMCB) within the School of Biological and Behavioural Sciences (SBBS) at Queen Mary. The project is focused
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About the Role The project “An Erlangen Programme for AI” (funded by the UKRI), will broadly involve applying advanced mathematical techniques for understanding training in neural networks, with