27 phd-computer-artificial-machine-human Postdoctoral research jobs at King's College London
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. Essential Criteria PhD qualified in relevant subject area (or pending results/near completion) Experience applying multi-modal models specifically in medical or clinical domains. Strong knowledge of MRI data
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. The post is offered at a competitive salary (Grade 6, Spine Point 33 on the KCL salary scale), and includes provisions for travel money, computer equipment and academic and leadership training. This is a
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to advancing precision medicine through computational replicas of human physiology and healthcare systems. The department brings together expertise in engineering, medical imaging, computational modelling, and
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. The post is offered at a competitive salary (Grade 6, Spine Point 33 on the KCL salary scale), and includes provisions for travel money, computer equipment and academic and leadership training. This is a
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About us The Faculty of Natural, Mathematical & Engineering Sciences (NMES) comprises Chemistry, Engineering, Informatics, Mathematics, and Physics – all departments highly rated in research
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results in top-tier international conferences and journals, and participate in applying for domestic and international patents. Assist in supervising PhD and Master's students, and participate in
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following skills and experience: Essential criteria 1. A completed, or close to being completed, PhD in computer science, economics, operations research, or a related field. 2. Expertise in
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molecules. The post is to be held in the Department of Physics at King’s College London, UK, in the research group of Prof. Joe Bhaseen. The post forms part of a UKRI (EPSRC) programme grant on “Quantum Many
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, charitable trusts and foundations, international Universities and industry. The post holder will work as part of a recently funded EPSRC Programme grant, undertaking research into Nanotheranostics, support
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international team - including Dundee, EMBL-EBI and German Bioimaging - to develop next gen file format strategies and data management for existing large complex multimodal human tissue bioimaging datasets at KCL