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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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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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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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an increase in coordination in the protection scheme. This PhD project seeks to address these challenges through comprehensive electromagnetic transient (EMT)-type computer simulations by developing
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PhD studentship in Computer Science: From Formal Requirements to Specification-based Automated Testing for Safety-Critical Medical Device Software Certification Award Summary 100% fees covered, and
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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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that respects both user preferences and legal requirements. Your research will create it. What makes this different This isn't a typical PhD. Through structured industrial secondments (several weeks yearly
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of lightweight, logic-based machine learning approaches. In addition, agents must support collective decision-making to achieve system-wide optimisation rather than isolated, local improvements. Finally
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and architectures that support efficient, secure, and scalable machine learning operations (MLOps) across resource-constrained environments for Edge AI. Ethical, and responsible FL for healthcare: In
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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