116 postdoc-parallel-computing Postdoctoral research jobs at University of Oxford in Uk
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programme grant with partners across the UK to facilitate the use of hydrogen for aviation, and in particular the icing vulnerability of heat exchangers and parts of the airframe. You will work to generate
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on evaluating the abilities of large language models (LLMs) of replicating results from the arXiv.org repository across computational sciences and engineering. You should have a PhD/DPhil (or be near completion
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engineering, computer science or other field relevant to the proposed area of research. You should have a good track record of robotic publications/presentations in the field of healthcare, possess sufficient
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shelves, the breakup of which can speed up flow of grounded ice and affect global sea level, and on the highly specialised Antarctic biodiversity. This ambitious programme brings together leading UK (BAS
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We are seeking a creative and highly motivated postdoctoral researcher to join the Turing AI World-Leading Fellowship research programme led by Professor Alison Noble. This exciting and ambitious
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have completed, or be close to completing, a PhD/DPhil in a relevant quantitative field such as computational social science, computer science, or cognitive science. They will have a demonstrable track
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We are looking to appoint a postdoctoral researcher, to work with a group of UK Higher Education Institutions to deliver a programme of mental health research. The work is funded by the Medical
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computational workflows on a high-performance cluster. You will test hypotheses using data from multiple sources, refining your approach as needed. The role also involves close collaboration with colleagues
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learning, at the intersection of reinforcement learning, deep learning and computer vision, in order to train effective robotic agents in simulation. You should hold a relevant PhD/DPhil (or near completion
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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