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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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networks for real-time, adaptive diagnosis. b) Uncertainty in Dynamic Environments: Runtime uncertainties require sophisticated risk modeling; we will employ Bayesian deep learning and deep reinforcement
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become living examples of a highly skilled workforce delivering an equitable energy transition so that Net Zero is inclusive for all. The efficient extraction of heat from deep geothermal energy systems
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). Additional project costs will also be provided. Overview Offshore Floating Wind (OFW) is key to unlocking deep-water renewable energy and achieving the global Net Zero targets. However, dynamic power cables
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on epithelial barrier integrity, inflammation, and host transcriptional responses. The project offers interdisciplinary training in bioinformatics, advanced statistics and machine learning, anaerobic microbiology
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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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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