645 engineering-computation-"https:" "https:" "https:" "https:" "https:" "University of Minho" PhD positions in United Kingdom
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individual with a 1st or a 2:1 degree from Mechanical, Manufacturing, Mechatronics Engineering, Computer Science or other relevant field. The candidate should have excellent analytical and communications
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high 2:1 in computer science, mechanical engineering, or aerospace engineering. You should have programming experience applied to physics/engineering problems and/or experience with machine learning and
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A fully funded PhD studentship is available for outstanding candidates to undertake cutting-edge research leading to a PhD in Chemical and Biomedical Engineering. The successful candidate(s) will
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, Computer Science and the Biosciences. You will be supervised by Amanda Wright (Optics and Photonics Research Group, Faculty of Engineering), Mike Somekh (Optics and Photonics Research Group, Faculty
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for applicants with a degree in Computer Science, Mathematics, Physics, or Engineering. Prior experience in AI is necessary. Prior experience in tomographic imaging and medical physics would be advantageous but
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Academy’s Training Programme, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career
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, and travel related to the project. Overview ReNU+ is a unique and ambitious programme that will train the next-generation of doctoral carbon champions who are renowned for research excellence and
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Are you an outstanding and ambitious engineering or computer science graduate looking for the next challenge? Do you want to work at the frontier of artificial intelligence and robotics to enable
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in the groups of Dr Florence Hardy and Prof Anthony Green, University of Manchester, as part of the cross-institutional BioAID Doctoral Training Programme, including world-leading experts from Queen's
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at the intersection of theory, computation, and high‑fidelity simulation, the successful candidate will contribute to the development of a novel ensemble-based framework for analysing driven perturbations in wall