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in conjunction with the industry partner, this PhD project aims to develop a reliable numerical modelling framework capable of: (i) simulating coupled heat and fluid flow within deep geothermal
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and critical infrastructure simulation. Training on formal verification methods (probabilistic model checking) and AI safety compliance (EU AI Act standards) will also be provided. Student Applicant
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fibroblast identity and function within liver tumours and how these cells shape anti-tumour immune responses. The student will use in vivo cancer models, spatial tissue analysis and immunological profiling
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Element Method (CFD-DEM) model to simulate and optimise the high-temperature chlorination process used by Krystal Technology Ltd, a UK-based leader in high-purity quartz production. This project will
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the Intelligent Systems Research group (with extensive experience in deploying machine learning models for edge devices and microcontrollers and building simulation environments for IoT), and our extensive network
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numerical simulations; and (ii) quantify the influence of parametric uncertainty on geothermal system performance using these surrogate models, enabling rapid assessment of design and operational scenarios
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pressures from climate change, urbanisation and ageing infrastructure. Although high-fidelity numerical models can simulate hydrodynamic and pollutant transport processes, their computational cost limits
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a robust modelling framework to simulate future changes in water resources in North and East England, using a combination of physically-based hydrological modelling tools and water system models
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their operational reliability. The PhD student will combine mathematical models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify
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: · Learning how to express software requirements precisely using formal models. · Using these specifications to automatically generate test cases for software systems and code. · Exploring how test