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. Quantifying the impact of parameter uncertainty on system performance typically requires repeated evaluation of computationally expensive numerical models, which may be impractical within project timescales
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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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and optimization but lack frameworks to continuously verify AI safety in operational contexts. This project aims to develop a dynamic validation framework for AI systems using high-fidelity digital
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risks to public health, ecosystems and urban water environments, particularly under pressures from climate change, urbanisation and ageing infrastructure. Although high-fidelity numerical models can
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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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for all. This PhD project aims to develop a robust numerical modelling framework to improve understanding and prediction of heat and fluid flow in deep geothermal reservoirs. Using geological, hydrological
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framework capable of accurately predicting pollutant transport and dispersion in coastal waters. By combining high-fidelity numerical simulations with data driven surrogate models, the proposed research aims