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the Cardiac Electro-Mechanics Research Group (CEMRG) at Imperial College London which applies statistical, machine learning and simulation approaches to combine experimental and clinical data with physics and
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, and has done a PhD in the field of computational neuroscience. The ideal candidate has expertise in computational neuroscience, in modelling reinforcement learning, in comparing modelling and
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, including exploitation of machine learning based algorithms related to tracking and vertex finding. This will include simulations as well as integration of algorithms into electronics hardware and testing in
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developing machine learning or data science approaches for patient stratification and genetic association analyses using cardiac magnetic resonance imaging in biobank populations. Successful applicants will
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modelling, graph theory, probability calculus, optimal transport, optimization and feedback control, machine learning, and a knowledge of distributed ledger technologies. A track record of publication in
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models, design of experiments, and/or symbolic machine learning. See job description for full list of requirements. *Candidates who not yet been officially awarded their PhD will be hired at Research
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discipline. Research interests that will contribute to the strategy of the department. Relevant underpinning topics include: Automation, Design, Dynamics, Fluid Mechanics, Machine Learning (applied
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to carry out Human Activity monitoring at scale in complex environments. This project aims to combine mmWave RF Sensing, enhanced using Reconfigurable Intelligent Surfaces (aka IRC) and machine learning
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. The candidate should have a PhD and research experience in machine learning, bioinformatics and/or cancer research. Additional experience in software product development is desirable but not compulsory
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been officially awarded their PhD will be appointed as a Research Assistant within the salary range £43,003 - £46,297 per annum Experience in computer programming (in particular Python), is essential