173 cloud-computing-"https:" "https:" "https:" "https:" "https:" "https:" "https:" research jobs at University of Oxford in United Kingdom
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to develop a personal research programme in observational or theoretical cosmology, with a particular emphasis on ultra-large-scale cosmology (including primordial non-Gaussianity and horizon-sized effects
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our multidisciplinary team working at the interface of epidemiology, data science, and public health policy. The successful candidate will develop and apply advanced mathematical and computational
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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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, and intervening in the internal representations and computations of large-scale AI systems. The project explores areas including automated circuit discovery, structured capability hypotheses from
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unique opportunity to work at the forefront of therapeutic genomics, leveraging large-scale functional genomic datasets and cutting-edge computational resources, including university HPC clusters and AWS
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Applications are invited for a Health Economics researcher to join a team led by Associate Professor Koen Pouwels. This research programme focuses on evaluating the cost-effectiveness of infectious
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Postdoctoral Researcher. The group aims to identify, understand, and develop therapies for rare genetic disorders. The group is primarily computational but partners with multiple international labs (including
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computational statistics, statistical machine learning, simulation and statistical programming). The postholders will join the dynamic and collaborative Department of Statistics. The Department carries out world
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archaea, many of which also use histones as major chromatin building blocks, and non-model bacteria. In the lab we combine a variety of computational and experimental techniques, including phylogenomics
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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