48 parallel-computing-numerical-methods "Prof" Postdoctoral positions at KINGS COLLEGE LONDON
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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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: 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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, which both contributes to public/policy debates, and provides high-quality contributions to academic debates. You will work closely with Prof. Ben Geiger and Prof. Karen Glaser as part of the Centre’s
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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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of Arts & Humanities. We are internationally recognised for our interdisciplinary research and teaching at the intersection of the humanities and computational methods. The successful candidate will join
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or pathway inference tools Experience working in high-performance computing or cloud environments Interest in developing novel computational or statistical methods for muscle biology Enthusiasm for open
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developing novel computational or statistical methods for muscle biology Enthusiasm for open science — sharing code, data and reproducible research practices Downloading a copy of our Job Description Full
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modeling. The role involves developing and implementing computational methods to integrate single-cell and spatial transcriptomics, proteomics, metabolomics, and metallomics data. Using advanced techniques
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highly motivated postdoctoral researcher with expertise in psychopharmacology and an interest in cannabinoid/cannabis, paranoia, psychiatry, and neuroscience to join our team at the CNS led by Prof Mitul
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transplant rejection through cutting-edge spatial multi-omics and computational metabolic modeling. The role involves developing and implementing computational methods to integrate single-cell and spatial