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modelling, CFD mesh generation in complex 3D geometries. Proficient in handling large data sets and the ability to analysis and interpret results. Experience in commercial CFD package such as ANSYS package
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Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and data integration. Knowledge
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: Erlangen Programme for AI” This is a 5-year programme supported by the EPSRC and is a collaboration of mathematicians and computer scientists at the University of Southampton, the University of Oxford (lead
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engineering, data analytics, games development, amongst others. This a great opportunity to embed yourself in the knowledge exchange ecosystem of digital innovation. The position will involve working closely
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proposals for own or joint research, with assistance of a mentor if required Contribute to writing bids for research funding Analyse and interpret data Apply knowledge in a way which develops new intellectual
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they are no longer eligible for the SFVS to see if things change. Quantitative and qualitative data will be collected through interviews and focus groups. A Research Fellow will also be appointed at Northumbria
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, large-grant project on the epidemiology of bovine tuberculosis in wild badgers, using state-of-the-art Bayesian modelling approaches to study the drivers of infectiousness and transmission of infection in
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collaboration that has mapped emotional hotspots in four cities in the UK and EU using spatial analysis of social media data. The next phase of the project aims to incorporate explainable AI (XAI) to interpret
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analyst for an ARC South London study on multi-morbidity in children using CPRD data. The ARC South London Children’s and Young person theme is important as many children in south London experience high
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completion) relevant to research area (immunology). Expertise in flowcytometry and/or fluorescent microscopy. High level of analytical capability, especially in the understanding and analysis of large data