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Field
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addressing the behaviour of thin foil materials for aerospace forming applications. The successful candidate will have a first-class or upper second-class honours degree in mechanical engineering or a related
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or international equivalent, in a subject relating to neuroscience or neural engineering. Further qualifications such as an MRes is advantageous. The candidate must have or be willing to learn computational methods
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The project: As wearable technology becomes increasingly ubiquitous in our lives, it is urgent we better understand how we might use the technology and how the technology can enhance our lives
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synthetic groups at the University of Cambridge, University of Oxford, Hasselt University, Chemnitz University of Technology, Princeton University and Georgia Tech. Who we are looking for: Motivated students
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first-class or upper second-class honours degree in mechanical engineering or a related subject. This studentship will attract a stipend of £20,480 for four years. The position arises from an engineering
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sustainable engineering strategies to boost their performance to deliver benefits for the environment and society. The outputs of this work will support practitioners in making informed investment decisions
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to an imperfect combustion in engines) and those emitted outside the exhaust (linked to the abrasion of tyres and the wear of brakes). The dynamics of exhaust and non-exhaust pollutants released into the atmosphere
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Application deadline: All year round Research theme: Systems and Control How to apply: uom.link/pgr-apply-2425 This 3.5 year PhD project is funded by The School of Engineering and is available
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this testbed available to users for testing hardware and applications. NPL will lead on the testing and security evaluation of the testbed and collaborate widely on the technology development. The student’s
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
engineers detect faults earlier, track system degradation, and make better-informed maintenance decisions. But how can we turn this complex information into something reliable, explainable, and actionable