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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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emergency responders. By combining global landslide data, innovative machine‑learning methods, and new ways of representing runout, the research will produce faster and more reliable nowcasts for use
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knowledge. This project requires specific and essential skills; however, these can be learnt throughout the PhD and with help from supervisory team. These may include: · Difficulties with learning how
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theory, Bayesian inference, Monte Carlo simulation, and statistical analysis of subjective data. Data science and machine learning - big data analytics, surrogate modelling, digital twin development, and
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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