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for cardiovascular applications. You will build on our group’s expertise on Physics-Informed Machine Learning (PIML), a powerful approach that combines data-driven AI with the rigour of physical and physiological
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testing strategies. About you The successful candidate will have: A Masterʼs degree and/or Bachelor’s degree in epidemiology, public health, health data science, health informatics, health economics
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DTU, Department of Civil and Mechanical Engineering , the Section for Manufacturing Engineering invites applications for a PhD position (3 years) on the topic of simulation of process induced
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international collaborations with clinicians, regulators, policymakers, and industry partners. You must have a strong background in machine learning, computer vision, and medical image analysis, with publications
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properties of cirrus, but we are currently limited by a lack of observational data. This project will fill that gap, using existing aircraft and satellite measurements to determine whether aircraft exhaust is
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programme’ page, please select (Neuroimaging) Research MPhil/PhD (Full-time). More information on the department and the programme is available at the departmental prospectus page here: https://www.kcl.ac.uk
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine
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. The School of Architecture, Computing and Engineering (ACE) at the University of East London (UEL) is deeply embedded in London’s dynamic and diverse communities. Known for its innovative, impact-driven
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are looking for: A PhD (or have submitted your thesis before taking up the role) in Materials, Physics, Chemistry or a closely allied discipline Expertise in 4D-STEM data acquisition Expertise in cryogenic
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an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine learning techniques and experience with coding in Python is beneficial (but not a strong requirement