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candidates to work with our interdisciplinary and collaborative team on a project on research and development of innovative surface treatments of advanced engineering materials. You will be based in Manchester
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capture technologies. In this project, you will: Develop a 3D Digital Model: Create an advanced computational model of high-pressure mechanical seals. Apply Computational Fluid Dynamics (CFD): Simulate gas
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for analysing complex materials, structures and model validation. The DIC community has developed guidelines to ensure robust measurements, continually advancing standards through ongoing challenges. In
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, low sputter yield, and excellent thermal conductivity. However, integration of W armour to structural materials in the first-wall (FW)/blanket remains a challenge due to the absence of robust dissimilar
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
this, you will work with emerging AI techniques such as large language models, which can interpret technical documentation, and knowledge graphs, which help structure and connect engineering knowledge
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an exciting opportunity to undertake industrially linked research with leading engineers at the UK's premier hub for advanced manufacturing, i.e., Manufacturing Technology Centre (MTC). and with academics
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We are pleased to announce PhD studentship project in “Advanced Composites Development for Hyper-velocity Impact Protection of Space Satellites Structures”. This is an exciting PhD research
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to reduce AC losses and boost power density. Today's modelling tools are not yet equipped to fully explore or optimise the flexible structures and manufacturing process of Litz wires. This studentship offers
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need for materials that can endure extreme conditions while maintaining structural integrity. This Ph.D. project will explore the failure mechanisms in refractory metals and advanced ceramic coatings
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer