42 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"SUNY" Postdoctoral positions at University of Oxford
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Oxford Population Health (the Nuffield Department of Population Health) provides an excellent environment for multi-disciplinary research and teaching and for professional and support staff. We work together to answer some of the most important questions about the causes, prevention and...
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We are seeking two full-time Postdoctoral Research Assistants in Machine Learning to join the Foerster Lab for AI Research group at the Department of Engineering Science (central Oxford). The post
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to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
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* together with relevant experience. You will have a strong technical background in machine learning, especially RL and LLMs. An ability to work independently and as part of a collaborative research team is
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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