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applications, and is intended to be a collaboration with Rolls-Royce and the UK Atomic Energy Authority. Supervisor: Mirco Magnini PhD Project Description The aim of this PhD is to robustly validate and
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, tailored to the needs and aspirations of the successful applicant. More information on the academic supervisor and EDC collaborations with JLR are available here: https://www-edc.eng.cam.ac.uk/people/nc266
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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novel computational imaging and sensing techniques for compact imaging systems. These systems are applicable to all sectors which require compact imaging specifications, but will have a primary focus on
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novel computational imaging and sensing techniques for compact imaging systems. These systems are applicable to all sectors which require compact imaging specifications, but will have a primary focus on
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disciplines to contribute to world-changing research in the development and deployment of trustworthy AI. The scholarship programme is part of the University’s internationally recognised Cyber Security Global
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The project: Motivation The development of hydrogen energy technologies is central to the UK government's ten-point plan for a green industrial revolution, targeting a 40% reduction in gas
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environments, highlighting potential cross-disciplinary synergies. The outcomes will enhance the security, dependability and trustworthiness of AI applications. The project will be conducted in collaboration
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for the probability of occurrence of FBO responses. Applications are invited for a fully fundedPhD studentship (UK or EU or International) working on the project. The PhD student will join Rolls Royce Computational
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Applications are invited for a PhD studentship in the Department of Computer Science at City, University of London. The successful candidate will work on developing a novel AI-powered conversational