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when the project commences. Applications are invited from candidates with (or who are expected to gain) a first-class honours degree or equivalent in a suitable field such as engineering, computer science
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sports sciences. The training and research programme: The successful candidate will join the EPSRC Centre for Doctoral Training programme in Photonic and Electronic Systems programme (PES CDT). Further
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, economists, and computer scientists at Royal Holloway.The position will be supervised by Professor David Levine (Economics), but the successful student will also benefit from the expertise provided by members
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Civil Engineering or Computational Physics. They should demonstrate aptitude for original research. The candidate should possess a good understanding of construction processes and data processing
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prediction. This project is a collaborative effort between the School of Computing & Mathematical Sciences (CMS) and the School of Engineering (SoE) to utilise expertise and facilities between two schools
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to power engineering, mathematics, computing and energy economics. The successful candidate will have excellent understanding in the fields of power system operations and economics. Experience in data
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, or higher) degree in Mathematics, Computer Science, Statistics, Engineering, Physics, or related STEM areas. Scholarship: The studentship is for 3 years and will provide an annual tax-free stipend of £20,662
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machine learning to CFD-generated datasets. The ideal applicant is a fresh graduate in engineering or in a closely related discipline, with a track record of achievements at the top of their cohorts
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for postdoctoral roles must hold a PhD or equivalent qualification in biology, epidemiology, computing, behavioural science or closely related disciplines, and should have a strong track record in data science
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methods could alleviate this limitation by leveraging large amounts of un-annotated datasets. These techniques remain however largely unexplored in the field of medical video analysis. The School of Science