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Field
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complex input. For instance, in physics-informed ML, in addition to data examples used by a standard ML setup, domain knowledge serves as an additional input. It can be in an explicit form of rigorous
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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
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use this formalisation to encode our STV algorithm on encrypted ballots. This approach aims to ensure both the correctness and privacy of the tallying process, paving the way for verifiable and secure
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that
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a collaborative studentship between the University of Edinburgh and the National Physics Laboratory (https://www.npl.co.uk/ ). The position will be registered and hosted at the University of Edinburgh
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subject relevant to the proposed PhD project (inc. Mechanical Engineering, Aerospace Engineering, Chemical Engineering, Applied Mathematics and Physics). Ability to undertake scientific programming (e.g. 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
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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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modelling tools to understand and tailor the physical and chemical interactions at the interfaces within metascintillators. Cranfield University’s Centre for Materials is internationally recognised
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digital twins, and life cycle assessment (LCA). A central component of the research will be the development of digital twins to simulate the entire production process, from raw materials to final product