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positions in High-performance computation and machine learning, Chemical Physics, Physical Chemistry, Nano-science, Biophysics, Statistical Physics and Complex Systems. The positions are normally for one year
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, United States of America [map ] Subject Areas: Computer Science Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Appl Deadline: none (posted 2025/08
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to adel.bibi@eng.ox.ac.uk For more information about working at
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curious to deliver work that matters, your journey starts here! The Department of Electrical and Computer Engineering (ECE) ranks among the best in the country. Our research programs are at the forefront
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successful candidate will develop and apply computational approaches to chemical datasets, with artificial intelligence/machine learning (AI/ML) being a major focus. Many of the laboratory's interests center
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to underfill at Grade 6 (£34,982 - £40,855 p.a.) if candidate holds a relevant degree and is working on PhD/DPhil) together with established knowledge in computer architecture and hardware security, significant
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Location: Parks Road, Oxford, OX1 3PJ About the role We are seeking a full-time Postdoctoral Researcher to join the Oxford Secure and Advanced Computer Architecture Research (OSCAR) group
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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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on and defensive mechanisms for safe multi-agent systems, powered by LLM and VLM models. Candidates should possess a PhD (or be near completion) in Machine Learning or a highly related discispline. You
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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional