72 computational-physics-simulation-"Prof"-"Prof" PhD research jobs in United Kingdom
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ABOUT VINUNIVERSITY’S RESEARCHERS 200 VinUniversity is proud to launch the Researchers 200 Program, designed to attract exceptional early-career researchers from around the world to join our vibrant
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Fixed-term: The funds for this post are available for 12 months A position is available for a Postdoctoral Research Associate (Postdoc) in the forthcoming Aspirational Computing Lab in
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used. AI methods for generating regulatory hypotheses between genes, hormones and physical properties will also be developed. Applicants must have/be close to obtaining a PhD or MPhil in Computational
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used. AI methods for generating regulatory hypotheses between genes, hormones and physical properties will also be developed. Applicants must have/be close to obtaining a PhD or MPhil in Computational
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simulations to improve on existing power usage models. This research will be a key component of making computing more sustainable by providing novel insights into the energy usage of scientific software and
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: Essential criteria MSc. in Neuroscience, Physics, Computer Science, or a related field Strong background in computational neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and
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for specific questions. For more information about the School of Computing, please click here To apply, please complete an online application and upload a plain text copy of your CV (2 pages) and covering letter
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28 Aug 2025 Job Information Organisation/Company KINGS COLLEGE LONDON Research Field Computer science Physics Researcher Profile Recognised Researcher (R2) First Stage Researcher (R1) Country
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems