35 computational-physics-"https:"-"https:"-"https:"-"https:"-"CEA-Saclay" Postdoctoral positions at KINGS COLLEGE LONDON
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: The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out research in neuro-symbolic AI, with a focus on using generative and
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they are inhomogeneous and out-of-equilibrium, and they are well described via hydrodynamics theories. They will be supported by EPSRC under grant UNIHYPE: Universal hydrodynamic principles and emergent physics, EP
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About us A post-doctoral research associate position is available at the Photonics & Nanotechnology group, Physics Department, King’s College London, funded by the EPSRC Programme Grant Catalysis
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6 Nov 2025 Job Information Organisation/Company KINGS COLLEGE LONDON Research Field Physics Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Country United Kingdom
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20 Jan 2026 Job Information Organisation/Company KINGS COLLEGE LONDON Research Field Computer science Researcher Profile Recognised Researcher (R2) Established Researcher (R3) Application Deadline
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interdisciplinary research and teaching at the intersection of the humanities and computational methods. The successful candidate will join the Computational Humanities research group, a vibrant and collaborative
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and disease and apply this knowledge to the development of new and innovative clinical practise, alongside providing a rigorous academic programme for students. About the role Dr Seaborne’s group
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United Kingdom Application Deadline 2 Dec 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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12 Nov 2025 Job Information Organisation/Company KINGS COLLEGE LONDON Research Field Biological sciences Computer science Mathematics Researcher Profile Recognised Researcher (R2) Established
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University (USA), and Google Deepmind (London). About the role The PDRA will lead the development of new computational and mathematical models to quantify and predict infectious disease risk, particularly for