370 engineering-computation-"https:"-"https:"-"https:"-"https:" Fellowship positions in United Kingdom
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offloading strategy via simulations and live demonstrations. Job Requirements A Ph.D degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical
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& multidisciplinary research programme focusing on evidence generation to further methodology research and to enable the design & development of in-vitro diagnostics and medical devices for patients and the NHS. NIHR
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-doctoral researchers and engineers, 3 support staff and 19 research students. Beyond kaon physics, our present activities include ATLAS and LHCb experiments at the LHC, the future Brookhaven Electron Ion
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, including dealing with problems that may affect the achievement of research objectives and deadlines. • Contribute to the planning and organising of the ReLiB research programme. • Co-ordinate own work
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Job Description Position Details School of Metallurgy and Materials, College of Engineering and Physical Sciences Location: University of Birmingham, Edgbaston, Birmingham UK Full time starting
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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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development programmes. The fellowship is tenable for one year. On an exception basis, a two-year programme may be supported. Service Commitment One year for every year of sponsorship. Application Process
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have a strong background in mechanical/electrical engineering and should hold a postgraduate degree in business administration or economics. Also, knowledge in the following areas is required: Conducting
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Clinical Research Fellow (BRC Arthritis) - Department of Inflammation and Ageing - 100873 - Clinical
(with the Universities of Glasgow , Oxford and Newcastle); NIHR Healthcare Technology Cooperative (Trauma); and the Scar Free Foundation Burns Research Centre, as well as our key role in the NIHR
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the development of microbial protein production processes. Leveraging advances in modern AI, the platform will consist of a Waste to Protein AI Engine coupled with an adaptive user interface enabling users to input