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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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us to run large numerical simulations with billions grid points on mixed computer architectures including CPU and GPU machines. A current project is preparing the code set for the next generation of
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programming skills (in Python, R or similar) and experience with large-scale data analysis using bioinformatic tools and pipelines to analyse and interpret biological data Attributes and Behaviour • Be
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quantitative and qualitative data. This project is likely to involve undertaking a systematic review; interrogating and analysing large-scale healthcare datasets; and using qualitative methods, such as
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environment. Accurately predicting flow and heat transfer in these systems is critical for safety, performance, and design assessments, yet direct high-fidelity simulations, such as Large Eddy Simulation (LES
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landscape, from SMEs and start-ups to OEMs and large-scale global manufacturers. For more information please visit the MTC website . Contact For further information on this PhD position please contact Dr
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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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connectivity seen in large scale brain recordings. These describe correlations between brain regions and can evolve over tens of seconds, with essentially discontinuous shifts from one short term state
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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
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coefficients. This strategy carries large uncertainty and requires vast amount of expensive and time-consuming experimental data. Worse, sometimes the experimental data is simply inaccessible. The need for cost