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EPSRC ReNU+ CDT PhD Studentship: Physics-informed machine learning for deep geothermal systems under uncertainty. Award Summary 100% fees covered, and a minimum tax-free annual living allowance
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reservoirs. By embedding governing equations and boundary conditions directly into machine-learning models, the project aims to enable efficient exploration of high-dimensional parameter spaces without
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external control. Autonomous agents that can perceive, reason, plan, act, and learn, together with self-configuring, self-healing, and self-optimising behaviours, provide the foundational principles
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, which are often limited and costly. This project explores the challenges of deploying AI at the edge within the context of federated learning (FL). Topics of interest include, but are not limited
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devices, the research will integrate established classical protection schemes with data-driven methods, including artificial intelligence and machine learning. The proposed protection strategies
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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
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and pragmatic engineering. You'll learn what it takes to make research deployable and commercially viable. Who should apply We're looking for candidates with potential, passion, and preparedness—not
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work. The project will involve: Learning how to express software requirements precisely using formal models. Using these specifications to automatically generate test cases for software systems and code
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prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained using datasets generated by the high-fidelity numerical solver. The surrogate will emulate key
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equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained