116 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "UCL" research jobs at University of Oxford
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travel and Season Ticket travel loans. All applications must include a CV, Supporting Statement/Cover Letter. For further guidance and support, please visit https://www.jobs.ox.ac.uk/how-to-apply. Any
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research project lead by Oxford Materials (Professors Robert House (PI), Saiful Islam, Peter Bruce), with UCL Chemical Engineering (Dr Rhod Jervis) and 4 industrial partners that brings together expertise in
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We are looking for a Research Assistant Reporting to Prof Yee-Whye Teh. The post holder will be a member of Oxford Computational Statistics and Machine Learning (OxCSML) with responsibility
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reproducible infrastructure while collaborating closely with cross-disciplinary teams across genomics, epidemiology, machine learning, and biomedical science. The role will also involve mentoring junior
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
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application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal
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operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer
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functional genomics, bioinformatics, and machine learning to support the generation, interpretation, and screening of large-scale experimental and computational outputs. The successful candidate will
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science, engineering, or a related discipline, with significant postdoctoral research experience. The ideal candidate will have strong expertise in computational biology, machine learning, and quantitative analysis