17 parallel-computing-numerical-methods Fellowship positions at The University of Southampton
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Do you have a PhD in HCI, Computer Science, or Related? Are you Interested in innovating interactive technologies to help #MakeNormalBetter for all? You’re an excellent and committed researcher
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leverage both computational and experimental data. Candidates should also have experience of numerical modelling (e.g. CFD and FEA), working as part of a team, with industry and disseminating their research
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. You will hold a PhD or equivalent professional qualifications and experience in aeronautical or mechanical or software engineering, physics, or in computer science/numerical analysis. About the
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Lead research projects focused on understanding and redesigning enzymes using a combination of structural and computational approaches. Deliver high-quality structural biology, applying advanced methods
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This role builds on a newly funded NIHR research (NIHR305518, PI: Dr Valeria Parlatini) aiming at testing a new method to select ADHD medication based on pre-treatment clinical, cognitive and
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An exciting opportunity has arisen for a Research Fellow to work on a NIHR ARC Wessex adopted and NIHR PGfAR funded programme grant: Personalised social and self-management support for better living
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candidates with a background in civil engineering, preferably with experience in geotechnical physical, analytical and/or numerical modelling. You will be responsible for developing a simplified computational
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interventions for frailty, the development and delivery of a bespoke intervention designed for South Asian communities. The research will use a mixed methods approach to this feasibility study including
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using remote sensing techniques and data collection methods across the MENA region, under the guidance of the project director; 2) Participate in ground surveys and contribute to training programs in
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to work effectively as part of a team and demonstrate flexibility to meet project deadlines. Essential skills and experience include: Strong understanding of qualitative research methods Excellent