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This PhD project offers the opportunity to explore a rich and rapidly developing area of research where powerful mathematical ideas unlock exact solutions in quantum physics. Most physical systems
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, while simulations are subject to error due to uncertainty in nuclear data and unresolved physical processes e.g. thermal expansion and fine-scale inhomogeneities. Generating independent simulation
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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considered to be self-funded students for the purposes of admission. Applicants should have (or expect to obtain by the start date) at least a first class degree in an Physics, Mathematics, Electrical
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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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Kevin Wilson, School of Mathematics, Statistics & Physics Dr Holly Fisher, Population Health Sciences Institute Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree
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AI models more interpretable and reliable by incorporating known constraints or physical properties. The successful applicants will be part of the Prob_AI hub and the School of Mathematics
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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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define observable events based on expert knowledge and available evidence. Development of a post-race analysis structure, process and data ‘toolkit’ that can build on historical understanding of race
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51 Faculty of Physics Startdate: 01.09.2025 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.08.2029 Reference Reference no.: 4391 We are a