559 computer-programmer-"https:"-"UCL" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" positions at University of Oxford
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), which will inform the University’s plan of action in this area. We are looking for two individuals who have sufficient knowledge in this area of work, and are able to manage their own research and have
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• Working toward a doctorate in a specialist discipline • Possess sufficient specialist knowledge in the discipline to work within established research programmes • Ability to manage own
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executive education programmes through our trading subsidiary, OSBS Ltd, equipping leaders from around the world with the skills to create meaningful impact. Our Human Resources Team is central to
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. High level of computer literacy, including MS Office and web-based systems, including Excel, Word, Ms Teams and Outlook is essential for this role. You will have significant administrative experience in
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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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scope, plan, and manage the execution of detailed project plans to ensure delivery of key outcomes within agreed timelines and University standards and lead the project team, providing overall direction
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is a world leader in child health research and hosts internationally renowned research programmes in drug development, gastroenterology, haematology, HIV, immunology, neuroimaging, neuromuscular
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the post. In all cases, the primary consideration will be the ability to perform the job. As stated in the University’s Equality Policy and Equality, Diversity and Inclusion Strategic Plan, our commitment
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AI Research Coordinator – University of Oxford Oxford hosts one of the world’s most extensive and diverse AI research communities, spanning fundamental AI, ethics and governance, robotics, medicine, climate science and more. That breadth is a strength, but it also requires careful...
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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