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the “Dialling up Performance for on Demand Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust
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the programme code: 8080F Research area: Statistics Select ‘PhD Mathematics (Full Time)' as the programme of study You will then need to provide the following information in the ‘Further Details’ section: A
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a team to undertake a PhD in the Optics and Photonics Research Group (OPG), supervised by Dr. Mitchell Kenney alongside collaborators within OPG and Life sciences. (https://www.nottingham.ac.uk
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a team to undertake a PhD in the Optics and Photonics Research Group (OPG), supervised by Dr. Mitchell Kenney alongside collaborators within OPG and Life sciences. (https://www.nottingham.ac.uk
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Manufacturing” Programme Grant, which will place the student within an active and supportive team of 9 other PhD students, 15 postdoctoral researchers, 18 world-leading academics from 5 universities, and 11
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the experimental activity. The candidate will be joining a multidisciplinary team in a lab where we design, make and validate materials and structures. This PhD will expose the successful candidate to a
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integrating Machine Learning (ML) with physics-based degradation modelling will enhance early fault detection, reducing unplanned downtime. This PhD is hosted at Cranfield University, a global leader in
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to produce cutting-edge research. Prospective applicants must: Hold a good honours degree in an appropriate subject (including Computer Science, Physics, Maths, Engineering) Knowledge of modern machine
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Join us for this exciting self-funded PhD studentship on " Development of Sustainable and Cost-Effective Coatings to Mitigate Battery Thermal Runaway Propagation" in collaboration with