115 computer-programmer-"https:"-"UCL" "https:" "https:" "https:" "https:" "https:" "Dr" "FEUP" Postdoctoral positions at University of Oxford
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by ATi/Innovate UK and Rolls Royce and is fixed-term to June 2029. You will join a world‑leading programme advancing experimental and numerical methods to predict the impact performance of composite
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computational fluid dynamics and wind turbine fluid mechanics together with the ability to understand the aerodynamics of wind energy generation and floating body dynamics is essential. Informal enquiries may be
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of this position will be based in Oxford, with Dr Will Coulton, and the second 18 months at IPMU, with Dr Leander Thiele. Oxford hosts a large cosmology group with expertise in theoretical, observational, and
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as part of an CRUK Discovery Programme Foundation Award in close collaboration with Dr Robert Köchl between the Kennedy Institute of Rheumatology (KIR) and Kings College London (KCL). The focus
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to their ongoing research programme, which aims to unravel the complex mechanisms underpinning 3-dimensional growth in plants. This is a fixed term position for one year. About you The successful applicant will hold
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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We are seeking a talented and motivated postdoctoral researcher to join our Somatic Evolution Research group led by Dr Verena Körber . You will contribute in the research of somatic evolution during
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cutting-edge research at the intersection of Digital Sociology and Public Policy. The successful candidate will join a small but growing connected families research group led by Dr Ekaterina Hertog and Dr
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Lab (Principal Investigator: Dr Abhishek Banerjee) to lead experimental work on a Wellcome Trust-funded project exploring the mechanisms of behavioural flexibility. The project combines behavioural
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with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly