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related research strands, including: RAM Process Modelling: The development of advanced numerical models for RAM processes, incorporating complex phenomena such as chemically induced viscosity changes
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early as the advert may be removed before the deadline. High-dimensional computations are ubiquitous in science and engineering, often arising from models with numerous parameters. For instance
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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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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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restoration success and flood resilience elsewhere. Current models treat sites in isolation, lacking tools to predict these feedbacks. This three-year PhD will develop and apply cutting-edge numerical modelling
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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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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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integral methods, stochastic modelling, optimisation algorithms, or numerical simulation. Candidates with interest in electromagnetic theory, quantum mathematics, wave chaos and complexity, as
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, modeling and Remote-sensing to Transform carbon budgets, CLARiTy’ (https://www.schmidtsciences.org/vicc/) will reduce the persistently high land flux uncertainties in GCB by an order of magnitude. To achieve
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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