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the sustainability of the aerospace industry. The coatings will be manufactured using plasma spray and mini-combustion flame spray and examined in SEM/EBSD and tested in a burner rig and furnace thermal cycling. As a
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subsequent precipitation hardening process. In addition, promoting a circular economy in the aluminium industry by increasing recyclability and using more recycled aluminium is essential for saving resources
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
datasets with industry-aligned performance metrics. The successful candidate will join the Centre for Digital and Design Engineering (CDDE), part of Cranfield’s Manufacturing, Materials and Design theme
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How to apply: uom.link/pgr-apply-2425 How many positions:1 This is an industry funded PhD for home and overseas students. The successful candidate will receive a tax free stipend set at the UKRI
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system will manage and adjust the way information is used in real time. Cranfield University is a globally recognised postgraduate institution focused on applied research and strong links with industry
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to produce high-quality journal publications and scientific manuscripts. Experience presenting research clearly at international conferences and industry workshops. Proven ability to manage your time
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control of 3D printed pharmaceuticals and support broader adoption of additive manufacturing in drug development. The project would be ideally suited for someone with a strong chemistry or closely related
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; reflectionless (Huygens’) metasurfaces; PCB manufacture; and mm-wave experimental testing. In this project you will take this capability further. You will design a suite of mm-wave Huygens’ metasurfaces
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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Bath, England | United Kingdom | 2 months ago
support UK industry to reach net zero. Alongside their research, our PhD researchers gain valuable training in how to apply their research within the wider industrial system, including opportunities
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operational data and machine learning. You will be based at UCL mechanical Engineering, and collaborate with industry and port partners on system design, prototyping, and lab-based trials. Key responsibilities