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systems enables effective digitalisation and automation transition. This project aims to build a generative AI model and algorithm for optimising manufacturing systems’ engineering design and processes
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or Human Factors and have completed a PhD in which these subjects have been significantly applied. You will have experience and have demonstrated competence in good social science research design and methods
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inform current storage and access regimes. By intertwining engineering, chemistry and history, the supervision will ensure a comprehensive interdisciplinary research experience for the PhD student
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Reference numberSWEE0255 Entry requirements Applicants should have a first or second class UK honours degree or equivalent in a discipline related to agriculture, plant science, biology, or ecology. An MSc
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, Materials Science/Engineering, Aerospace Engineering, Physics, Chemical Engineering or related discipline. The candidates should be self-motivated, have good communication skills, and an interest in
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diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and
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upgrade water treatment infrastructure and increase its resilience using nanobubble technology as a chemical-free approach. The successful candidate will work closely with the water industry to develop
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social science discipline. Experience in qualitative research will be an advantage. This funded studentship is part of the Connected Waters Leverhulme Doctoral Programme, which is funding up to 18 PhD
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Engineering or aerospace or a related discipline. This project would suit those with a strong interest in aviation. Experience of computer programming (preferably in Java) would be an advantage. A logical
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conducted through the Rolls-Royce University Technology Centre based at Cranfield which has a strong collaborative history with Rolls-Royce in the area of aero-engine aerodynamics. This programme provides