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An opportunity to apply for a funded full-time PhD in the College of Health, Science & Society, UWE Bristol. The studentship will be funded by Alopecia UK . Ref: 2526-OCT-CHSS04 The expected start
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Deadline: 30 June 2025 A fully funded four-year PhD position is available to work on the project titled “Real-world quantum verification and benchmarking of noisy hardware”. This position is a
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clicking the 'Apply' button above. In addition, please provide: (1) a cover letter summarising your research interests and suitability for the position, and indicating with PhD project would be of interest
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to improve structural durability, reduce material consumption, and support the UK’s net-zero goals. Funding notes: The position includes a full scholarship for UK-based PhD candidates and a half-scholarship
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, there must be strategies in place supporting working individuals to promote their physical and mental health, alongside quality of life. Physical activity is universally recommended to reduce sedentary
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The project will work alongside Vishay Intertechnology, which brings together a number of world leaders in power semiconductors and energy conversion to develop solutions for automotive, aerospace, industrial and grid-level power electronics. The purpose is to improve the electric vehicles and...
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operation of autonomous systems in complex, real-world conditions. This PhD project aims to develop resilient Position, Navigation and Timing (PNT) systems for autonomous transport, addressing a critical
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in spin-dependent processes in materials and devices for energy technology. More information can be found on the group website: taitgroup.web.ox.ac.uk . Please contact Dr Claudia Tait (claudia.tait
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Project Overview This PhD project is part of an Innovate UK-funded research programme focused on developing a novel ammonia-fueled engine and generator set (genset) demonstrator for harbour and
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, flexibility becomes an essential feature, driven by the development of flexible components and open networks. Development of Digital Twins, capable of building dynamic models from information gathered form live