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Field
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modelling of NCLs within industry employs System Codes, which rely heavily on empirical and scale-dependent correlations obtained via experimentation. Unsteady Reynolds-averaged Navier-Stokes (URANS
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Research theme: “Groundwater remediation”, “Environmental application of nanotechnology”, “Groundwater modelling”, “Aquifer”, “MODFLOW model”, “Nanoparticle transport in porous media”, “Nanoparticle
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to develop a simple and reproducible cell-based model to investigate how the changes in blood flow associated with pre-eclampsia damage the syncytiotrophoblasts leading to the detrimental release of factors
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reduces computational cost and enables large-scale reactor simulations, current porous approaches, based on Reynolds-averaged Navier-Stokes models, rely on empirical correlations and assumptions that may
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health with the aim of creating healthier spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based
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for individuals living with multiple long-term conditions known as multimorbidity, and may lead to unnecessary polypharmacy. This PhD studentship aims to develop a Bayesian modelling framework to identify clusters
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model of the patients’ condition is required. This PhD will investigate how knowledge models can be built and maintained for healthcare applications. The project will explore the use of combining
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when,...
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In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when,...
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PhD Studentship: Knowledge models for healthcare digital twins and improved patient care pathways School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded Students