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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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healthcare technology. Project Overview Healthcare digital twins are virtual replicas that continuously assimilate patient data to provide personalised predictions and support clinical decisions. To enable
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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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areas. Cranfield is part of the national testbed for 6G, researching in the following areas of interest: Real-time specification of 6G telecommunication and edge computing services using Large Language
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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
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distribution. This process often takes place in large scale driers where the material is heated and broken up mechanically with mixing blades. However, under certain conditions the process can break down as the