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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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Fixed term for 30 months Reporting to the PI, Prof Erin Saupe, the post holder will be a member of a research group with responsibility for carrying out research for ERC grant ‘Determining the drivers of extinction across space and time’. The post holder will provide guidance to less experienced...
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Faculty of Engineering and Physical Sciences EPSRC Project Proposals 2026/27 (jobs.ac.uk) Project Link via the 'Apply' button above Project Title: Machine Learning Driven Corrosion Modelling in Bio
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Reporting to the PI, Prof Erin Saupe, the post holder will be a member of a research group with responsibility for carrying out research for ERC grant ‘Determining the drivers of extinction across space and time’. The post holder will provide guidance to less experienced members of the research...
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properties. Our group uses theoretical and computational chemistry, physics, and materials science in combination with chemical machine learning to explore and exploit diverse functional organic and hybrid
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Machine Learning Quantum Phase Transitions via Topology- and Symmetry-aware AI School of Mathematical and Physical Sciences PhD Research Project Self Funded Dr Marco Fazzi Application Deadline
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The Department of Statistics is recruiting to a Florence Nightingale Bicentenary Fellow in Statistical Machine Learning. This is career development positions intended to carry around half the
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Generalisable, Physics-Aware Machine Learning for Gas Turbine Engines School of Electrical and Electronic Engineering PhD Research Project Directly Funded UK Students Dr Will Jacobs, Dr Andy Mills
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Reference: 0181-26 Senior Research Associate in Machine Learning for Speech Processing Department: Phonetics Laboratory / Linguistics and English Language Contract: Full-time (1.0 FTE), fixed-term [18 months
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the noise associated with near-term quantum devices. This in turn offers an exciting new dataset from which it will be possible to use machine learning to train a more accurate functional for use in density