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Field
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supervisors spans five departments at University of Nottingham including Architecture and Built Environment, Electrical and Electronic Engineering, Mathematics, Physics and Social Sciences. The PhD programme
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
Christina Latsou 2nd Supervisors: Professor John Erkoyuncu , Dr Bernadin Namoano Entry requirements Applicants should hold a First or Upper Second Class UK Honours degree (or international equivalent) in a
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Medicine Research Centre (ReconRegen ) led by Professor Iain Whitaker, Advanced Imaging of Materials Facility (AIM ) led by Professor Richard Johnston and Swansea University's Simulation and Immersive
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personalized. The candidate will investigate textiles, computational design, and functional validation of hand-worn assistive devices, aiming to create textile exoskeletons that can conform to complex hand
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subject. This studentship will attract a stipend of £20,480 for four years. The position arises from an engineering research relationship developing between the University of Nottingham and ITP Aero UK. The
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research relationship developing between the University of Nottingham and ITP Aero UK. The studentship will be hosted at the Institute for Aerospace Technology at the University of Nottingham and the
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(including 7 full professors) and approximately 120 PhD students and post-doctoral research fellows. The group has excellent facilities for experimental work including approximately 2500m2 of research space
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150 members, with 18 academics (including 7 full professors) and approximately 120 PhD students and post-doctoral research fellows. The group has excellent facilities for experimental work including
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for candidates with a background in meteorology, climatology, physics, engineering and any related discipline, and a strong interest in applying advanced physical and computational methods to real-world
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty