65 parallel-computing-numerical-methods Postdoctoral positions at KINGS COLLEGE LONDON in Uk
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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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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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more effective screening and therapy. The postholder will focus on developing and applying advanced computer vision and machine learning methods for multimodal imaging and real-time analysis in
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the Department of Informatics, part of the Faculty of Natural, Mathematical & Engineering Sciences (NMES). The department is internationally recognised for its contributions to robotics, AI, and human-centred
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About us: Applications are invited for a Postdoctoral Research Associate to work on the Improving Communication with Adults with Learning Disabilities (ICALD) research programme, funded by
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English Ability to work in a team Desirable criteria Numerical skills, such as: Monte Carlo methods, Density Matrix Renormalisation Group or Truncated Conformal Space Approach Knowledge of quantum field
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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 Next
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techniques and numerical modelling. About the role A successful candidate will join the EPSRC-funded Programme Grant Next Generation Metamaterials: Exploiting Four Dimensions (META4D: www.meta4d.co.uk
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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 related to staff
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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