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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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PhD project (such as Electrical Engineering or Computer Science) is our standard entry, however we place value on prior experience, enthusiasm for research, and the ability to think and work
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to funding restrictions this PhD position is only available to UK nationals. As this position is sponsored by the MTC, any successful candidate would need to pass the sponsors own security checks prior
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
professional and transferable skill development, preparing graduates for careers in aerospace, engineering, and digital innovation. Throughout the PhD, the student will develop a broad set of skills, from
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim
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The position is open for both UK home and international candidates. PhD Start date: 1 October 2025 Application deadline: 10 June 2025 For further information please email Prof. Michele Degano: michele.degano
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to how well they meet the following criteria: A first class or strong upper second-class undergraduate honours degree in Engineering, Mathematics, Computer Science or Physics Excellent English written and
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electronics, embedded programming, signal processing, vibration measurement and analysis, maintenance engineering, and electro-mechanical engineering. Funding This is a self-funded PhD. Find out more about fees
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in a subject relevant to the proposed PhD project (such as mathematics or theoretical physics) is our standard entry, however we place value on prior experience, enthusiasm for research, and the
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Application deadline: All year round Research theme: Robotics, Smart Manufacturing How to apply:uom.link/pgr-apply-2425 No. of positions: 1 This 3.5 year PhD is funded by the Department