21 senior-lecturer-distributed-computing Postdoctoral positions at KINGS COLLEGE LONDON
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engineering, linked data, web technologies. About the role: The successful candidate will join the Distributed AI (DAI) group in the Department of Informatics, King’s College London. They will carry out
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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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cloud or distributed computing environments. Familiarity with self-supervised and contrastive learning techniques for aligning text and images (e.g., CLIP, SimCLR). Clinical experience, e.g., interaction
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hardware components, notably integrating communication, sensing and computing, are essential to ensure efficient spectrum utilization and reduce hardware costs in 6G networks. Besides connectivity and
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24 Dec 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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United Kingdom Application Deadline 4 Jan 2026 - 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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Application Deadline 14 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 related to staff
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the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description About us The
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United Kingdom Application Deadline 4 Jan 2026 - 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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. The successful candidate will work within a multidisciplinary team to unravel the metabolic drivers of HCC biology and transplant rejection through cutting-edge spatial multi-omics and computational metabolic