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The research in this doctoral opportunity will investigate the relationship between material elastic and thermal properties by using high resolution digital imaging under dynamic loads. Digital
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or mechanical engineering background. Experience of experimental aerodynamics, particle image velocimetry, flow or aerodynamic measurements, compressible flow, CAD would be an advantage. As part of this role you
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at Cranfield University within a supportive, cross-disciplinary environment spanning energy systems engineering, computational modelling, bioinformatics and food safety science. The studentship is funded by
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of first or second class UK honours degree or equivalent in a related discipline, science (chemistry/physics) or engineering. The ideal candidate should have some understanding in the area of materials
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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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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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the growing demand for sustainable AI-enabled systems, this PhD brings together low-power computing, energy-aware design, and thermal optimisation. You’ll work with advanced profiling tools, prototype long-life
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, as well as our specialist industrial network. The Centre for Defence Engineering and Physical Science at Cranfield University is a world-leading centre for research, education and consultancy on a
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second class UK honours degree or equivalent in a related discipline. This project would suit students with an aerospace or mechanical engineering background. Experience of computational fluid dynamics