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Field
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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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defined by Swansea University) in Engineering, physical sciences or similar relevant science discipline. We also welcome applications from graduates in computational science or mathematics. Note for
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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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Modern cyber-physical systems (CPS), such as UAVs, next-generation fighter aircraft, and command-and-control (C2) platforms, integrate digital computation with physical processes to make mission
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candidates with: • Relevant subject matter experience at required level (e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) • Willingness to adapt and work across different
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(e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) Willingness to adapt and work across different disciplines Ability to work independently and cooperatively Commitment
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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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dedicated PhD student with a 1st class or 2:1 degree in the field of Engineering, Mathematics, Physics, Architecture or Computer Science. A MSc degree in a relevant area is desirable though not necessary
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by the Ada Lovelace Centre and the University of Birmingham. This interdisciplinary project is ideal for candidates with a background in physics, materials science, chemistry, or computational science
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, computing, and energy economics. The successful candidate will have an excellent understanding in one of the following fields: power system operations, power system economics, linear programming, micro