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This PhD project will develop next-generation grid-scale energy storage solutions integrated into HVDC (High Voltage Direct Current) systems at the University of Edinburgh, in partnership with UK
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Joint Industrial and EPSRC-funded PhD studentship in the Synthesis and Processing of Novel Biomaterials, in partnership with Haleon Applications are invited for a PhD Studentship, with an October
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replacement, statistics and working with joint registry data. You will be based in the Integrative Musculoskeletal Biomechanics (IMSB) research group and join a thriving PhD research community in the School
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. The successful applicant will be required to start in October 2025 or as soon as possible thereafter. This is for a full-time PhD position. About Us At Edge Hill University we believe in the life changing
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short courses in the core subjects of this PhD programme including process intensification and green chemistry. This project is part of the Process Industries: Net Zero (PINZ) Centre for Doctoral training
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to funding restrictions, the position is only available for UK home candidates. As sponsored by MTC, the successful candidate would need to pass the sponsors’ own security checks before starting the PhD. PhD
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to apply: https://uom.link/pgr-apply-2425 How many positions: 1 Residency: UK students This 3.5 year PhD project is fully funded for home students; the successful applicant will receive an annual tax free
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
at BHAFC and a supportive supervisory team with expertise in sleep, sports medicine, sports science, and data analytics. The PhD researcher will design, conduct, and analyse data and review literature
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This PhD studentship is a part of Rolls-Royce sponsored research centre developing advanced control systems to enhance the performance and efficiency of new aerospace propulsion systems. We apply a
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of good-quality data is typically limited for high-value critical assets. This PhD project will focus on developing, evaluating, and demonstrating physics-informed machine learning or domain knowledge