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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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systems with "self-diagnosis" and "self-healing" capabilities. By integrating federated learning, graph neural networks, and blockchain technology, we will develop a framework that moves beyond static
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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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biosecurity strategy. We will support you to learn key technical skills including in synthetic biology, in vitro diagnostic assays, and designing your own diagnostic experiments. Training will include access
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academia, government, and industry, with opportunities to engage with Defra and APHA stakeholders and contribute to national biosecurity strategy. We will support you to learn key technical skills including
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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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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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: · 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