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define observable events based on expert knowledge and available evidence. Development of a post-race analysis structure, process and data ‘toolkit’ that can build on historical understanding of race
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Please quote reference: Quantum Computing at The School of Physical Sciences Duration: 3 years and 6 months. We are currently recruiting for the following full-time, fully funded PhD studentship
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degree or a Master’s programme and/or have worked for some years; or are currently enrolled in a PhD programme overseas Upon successful completion of the PhD programme, the scholar will be considered
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The Centre for Doctoral Training in Nanoscience and Nanotechnology (NanoDTC) at the University of Cambridge invites applications for its 3.5-year interdisciplinary PhD programme. The programme
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at the UKRI rate (£20,780 from 1/10/25). There is an enhanced budget for project costs (including travel) of £20,000. Applications are made to the Metamaterials programme for a PhD in Physics/Engineering. We
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aptitude is desirable. Skillset development: The research spans fundamental optical physics through to applications, and the student will develop a diverse and sought-after skillset during the PhD project
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Deadline: 12.12.25 How to apply: https://uom.link/pgr-apply-2425 For UK students This 3.5-year PhD studentship is open to Home (UK) applicants and EU students with settled status. The successful
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the link sent by email to start the application process. Please select ‘PhD in Physiology and Pharmacology (initial registration) (full time)’. Please include a personal statement (max 200 words) with your
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Royal Holloway, University of London | Kingston upon Thames, England | United Kingdom | about 12 hours ago
to completion of the PhD programme. The DTP will reserve up to 3 ring-fenced scholarships for home students from certain Black, Asian & Minority Ethnic groups. The eligibility criteria for these ring-fenced
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an important role in the efficient integration and management of solar energy in modern power systems. The studentship project aims to develop a novel PV forecasting model based on physics-informed neural