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flight. Applications are invited from candidates who possess (or expect to gain) a first-class honours MEng or higher degree or equivalent in Aeronautics, Mechanical Engineering, Robotics or related areas
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. Ideal candidates would have a strong background in Computer Sciences, Software Engineering, Artificial Intelligence, or related disciplines. Proficiency in Deep Learning and familiarity with tools such as
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(WIRe), a prestigious collaboration between Cranfield University, the University of Sheffield, and Newcastle University. The WIRe programme offers bespoke training that hones both technical and personal
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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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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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are expected to gain) a first-class honours degree or equivalent in a suitable field such as engineering, computer science, physics, or atmospheric science. Funding This studentship is for 3.5 years and will
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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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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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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