580 computing "https:" "https:" "https:" "IFM" Fellowship positions in United Kingdom
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Background To create and contribute to the creation of knowledge by undertaking a specified range of activities within the research programme of the Elementary Particle Physics Group. To work on detector
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doctorate in Classics or Philosophy (awarded since 1 October 2021), or close to completion (submission expected by 1 October 2026); have a coherent proposed research programme for the duration
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major, transformative UKRI-funded programme, tackling one of the most urgent global health challenges. The successful candidate will join a vibrant, interdisciplinary collaboration bringing together
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post or completed CCT and looking for the opportunity to undertake an out of program research project to result in the award of a PhD. You will have obtained MRCP or equivalent and be working towards
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rigorous simulations and live demonstrations. This position is ideal for a researcher with a passion for solving complex problems at the intersection of wireless communications, edge computing, and machine
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Employer and proud members of the Stonewall Diversity Champions Programme. We are committed to actively exploring flexible working options for each role and have been ranked in the Top 30 family friendly
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requirements and use resources effectively Understanding of and ability to contribute to broader management/administration processes Contribute to the planning and organising of the research programme and/or
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their skills outside PhD study, those who currently hold a research or academic-related position with a contract of 12 months or more will be considered to have achieved the aims of this programme and as such
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Coupled Plasma) torch design for high enthalpy thermal plasma flow. Developing/Validating computational models for a dual stage plasma torch. You should hold a PhD (or equivalent experience) in Plasma
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clinical base at the Maudsley Hospital and involves affiliated laboratories across King’s Health Partners. The programme is strongly lead by statistical modelling to predict not just Parkinson’s disease (PD