36 evolution-"https:"-"https:"-"https:"-"https:"-"https:"-"Newcastle-University" PhD positions at University of Nottingham
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simulation, composites manufacturing and advanced sensing techniques. The project will provide opportunities to develop skills in these areas and contribute to the development of the next generation composite
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efficiency, consistency and patient outcomes; they also introduce new cognitive demands, risks, and accountability challenges. This project aims to contribute to the development of human-centred, safe, and
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coupling. This PhD project focuses on the development of next-generation high power density EMS, to unlock a more compact and efficient energy ecosystem, where EVs do not just consume power, they help drive
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metabolomics and novel physiological MRI technique development within a translational research environment bridging analytical bioscience and neuro-oncology. Research Environment The successful applicant for a 4
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functional theory. In collaboration with Phasecraft, a leading quantum algorithms company, this project will explore the generation of new quantum computing datasets and the development of machine learning
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physiological MRI technique development within a translational research environment bridging analytical bioscience and neuro-oncology. Research Environment The successful applicant for a 4-year PhD studentship
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, including bespoke courses for Engineering researchers on academic writing, networking, and career development. The faculty also offers outstanding facilities and maintains strong partnerships with leading
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and training from both academic and industrial researchers, and gaining direct exposure to industrial CFD workflows and software development practices. Candidate Requirements We are seeking
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, those based within the Faculty of Engineering have access to bespoke courses developed for Engineering PGRs. including sessions on paper writing, networking and career development after the PhD
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learning, control theory, and embodied autonomous systems. The successful candidate will contribute to the development of learning-based control methods that are not only high-performing, but also safe