594 computer-programmer-"https:"-"FEMTO-ST"-"UCL" "https:" "https:" "https:" "https:" "https:" "P" Fellowship positions in United Kingdom
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for parental/caring leave etc). The Fellowship carries a stipend of £33,400 p.a. (subject to review). The appointment will be pensionable under the USS scheme, details of which are available here: https
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lecturers (£57,556 per annum). Application should be made via the online application system (via the above ‘Apply’ button). Details are available on the Trinity College website (http://www.trin.cam.ac.uk
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workstream of mTBI-Predict (https://mtbi-predict.digitrial.com/), with an expected start date of 02/01/2026 and a fix-term duration of 6-months. The focus of each position is slightly different with one Fellow
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turbulence modelling, CFD mesh generation and use of parallel computing Have relevant experience in working with aerosols and droplets Proficient in handling large data sets and the ability to analysis and
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will be given to applicants who have taken a career break. The University has a generous maternity policy**, and onsite childcare facilities. For further information please visit https
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. Desirable: D1. Evidence of participation in Audit / Research activities. D2. Evidence of a planned or proposed, programme of research. D3. Interest in and knowledge of the relevance of research. D4
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that are relevant to industry demands while working on research projects in SIT. About the DIGNIFIED programme The DIGNIFIED programme is dedicated to developing elderly-friendly, textured modified foods with
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. Bharathram Ganapathisubramani, collaborating with the European Space Agency (ESA) and NHS Hampshire Hospitals. More information about the project is available at https://project-pasta.com . Key
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engineering aspects to the project (around model validity) and computer science aspects (around parallel efficiency and computational time). You will be working closely with engineers at Rolls-Royce and with
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University of Surrey. You will be working on a 3-year project (funded by Innovate UK) looking at coupling of fluid and solid models and how we could use AI to speed up the computational time required. You will