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should have or expect to achieve, at least a 2:1 (or equivalent) in any engineering degree programme, physics or mathematics. English language requirements: Applicants must meet the minimum
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% of their time on teaching/related professional development. Teaching duties will be agreed annually with Programme Directors and tailored to the demonstrator’s expertise and career goals. Applicants will be
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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infrastructure. However, the increasing application requirements and rising threats from intentional interferences, spoofing, and cyber-physical attacks expose vulnerabilities in conventional GNSS-centric systems
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and advanced material design and fabrication. Through this multidisciplinary project, the student will develop expertise in: Hands-on experience with advanced computational physics and materials
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will
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and will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Ivan Rungger, National Physics
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including predictive modelling, computer vision and epidemiology. The student will join an established team of investigators, including statisticians, epidemiologists, image scientists, and clinicians
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a highly motivated candidate with: A first-class or upper second-class degree (or equivalent) in Materials Science, Chemistry, Physics, Chemical Engineering, or a related discipline. Experience in
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Computational verification of high-speed multi-material flows, where physical experimentation is highly limited, is seen as critical by the Defence Sector (source: the UK Atomic Weapons