36 post-doc-computer-science Postdoctoral positions at University of London in london
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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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About the Role We are recruiting an enthusiastic postdoctoral research associate to conduct a scientific programme of work focussed on pain mechanisms in epidermolysis bullosa, under the supervision
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these into industry by producing and validating a suite of exemplar organ-chip models of musculoskeletal tissues. About the School/Department/Institute/Project This post is within the School of Engineering and
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and Immigration website . Full-Time, Fixed-Term (18 months) Applications are invited for the post of Postdoctoral Research Associate in the Department of Mathematics for 18 months, starting 1st October
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for an outstanding, post-doctoral researcher with proven expertise and practical experience in relevant techniques including cell culture, organ-chip models, tissue engineering, and musculoskeletal biology. The PDRA
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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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are seeking to appoint a postdoctoral research associate as part of a UKRI Future Leader Fellowship funded research programme. The successful candidate will work as part of a team to develop and apply deep
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develop, synthesise and characterise materials for this project. About You The post is suited to a PhD graduate with a background in materials chemistry or a related discipline. If you have a vivid
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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