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identify your programme of study: Search for the ‘Course Title’ using programme code: 8020F Research Area: Biological Sciences Select ‘PhD Biological Sciences’ as the programme of study. You will then need
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, or physics. Prior experience in microstructural characterization, computational modelling, or metal forming is advantageous but not essential. Enthusiasm and willingness to learn are more important
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as well as increasing the impact of the research. The theoretical underpinning and novelty of the research will contribute to the academic fields of computer science, networked systems, and high
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with a strong commitment that supports equality, diversity and inclusion, and promotes open science and excellence in science. The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP
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Self-Funded PhD Research Studentship (February 2027 start) This form is only to be used by those self-funded applicants seeking a place on a research degree programme at Harper Adams University
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the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex. Only UK domiciled applicants who meet the following criteria may apply: Not already have a Master’s degree And Be
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(swallowing difficulties). The Norwich Research Park Biosciences Doctoral Training Programme (NRPDTP) is offering fully funded studentships for October 2026 entry. The programme offers postgraduates
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Applications are invited for a Research Associate in Climate Change and Computational Modelling. This is a postdoctoral-level appointment, but applicants within a few months of submitting their PhD
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3-Year PhD Studentship: Design and Autonomous Control of Soft Robotic Systems for Aero-Engine Repair
Supervisors: Dr Xin Dong and colleagues from the Rolls-Royce University Technology Centre (UTC) in Manufacturing and On-Wing Technology Overview Applications are invited for a fully funded PhD
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of Warwick, and Prof Emma Kendrick at the University of Birmingham. The project will be affiliated with the Faraday Institution FAST project and will combine advanced computational modelling with experimental