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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
are committed to fostering equity, diversity, and inclusion in our CDT program, and warmly encourage applications from students of all backgrounds, including those from underrepresented groups. We particularly
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Application deadline26 Nov 2025 Award type(s)PhD Start date26 Jan 2026 Duration of award3 years EligibilityUK, EU, Rest of world Reference numberCRAN-0015 Entry requirements Applicants should have a first or
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Cranfield University invites applications for a PhD funded by Thames Water through the Ofwat Innovation Fund. The studentship covers full Home tuition fees plus a tax free stipend of £24,000 per
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in our CDT program, and warmly encourage applications from students of all backgrounds, including those from underrepresented groups. We particularly welcome students with disabilities, neurodiverse
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in validation on EV cells. Applicants are required to self-fund their fees and living expenses during the study period. Thermal runaway in lithium-ion battery packs poses critical safety challenges in
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play a very vital role in the operation and availability of rotating machinery. Despite successful implementations in industrial applications, CM of bearings still poses many challenges. One
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additive manufacturing. This project will be closely aligned with the ATI research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects
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research program (I-Break: Wire-based DED Technology Maturation and Landing Gear Application) and other industrial research projects within WAMC. The student will become part of a diverse and dynamic
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within the University. The activity spans across land, off-shore, marine, air and space power and propulsion applications, with a particular specialisation in gas turbine engineering. This exciting project
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This self-funded PhD research project aims to advance the emerging research topics on physics-informed machine learning techniques with the targeted application on predictive maintenance (PdM