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for example synchrotron based experiments. More information on these facilities can be found; There will be many rewards: Working as part of a large and experienced team across the cohorts will ensure a
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Research theme: Nuclear thermal-hydraulics How to apply:uom.link/pgr-apply-2425 How many positions: 1 This 3.5 year PhD is fully funded, home students and students with settled status are eligible
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. when do we stop modelling? How do we track / score the quality of the model What is the required level of quality over time How can quality be brought to the required level Can Machine Learning, Large
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Supervisory Team: Prof Neil Sandham PhD Supervisor: Neil Sandham Project description: This project is focused on scale-resolving simulations (large-eddy and direct numerical simulation) combined
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to test data, which are therefore only useable within a narrow range of scenarios. These limitations result in the requirement of large number of high-cost experiments being conducted to populate the models
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the “Finances/Funding” field write “QIST PhD” Upload all required documents (CV, personal statement, degree certificates and transcripts). Contacts For further information about the programme at Sussex contact
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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University. Applications are invited for a PhD studentship in the Centre for Propulsion and Thermal Power Engineering, Cranfield University, in the area of gas turbine performance, diagnostics and prognostics
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learning models of quantum chemistry can achieve fast and accurate predictions, but comprehensive data sets for reaction barriers of large molecules simply do not exist. Several recent works have attempted
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advanced simulation methods, including Reynolds-Averaged Navier-Stokes (RANS), Direct Numerical Simulations (DNS), and/or Large Eddy Simulations (LES), will be employed to accurately model the complex flow