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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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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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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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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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-grade experience that employers value. The journey You'll develop machine-readable privacy rules, build core functionalities that audit and explain data-sharing decisions, prototype agent systems showing
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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