18 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" scholarships at Newcastle University in United Kingdom
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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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6.5 overall, with 5.5 or more in each sub-skill. How To Apply You must apply through the University’s Application Portal: https://applyto.newcastle.ac.uk/ In ‘Course choice’ tab: Type of Study
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· Interest/experience in health services research How To Apply You must apply through the University’s Application Portal: https://applyto.newcastle.ac.uk/ In ‘Course choice’ tab: Type of Study - ‘Postgraduate
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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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and to study on this programme. How To Apply You must apply through the University’s Application Portal: https://applyto.newcastle.ac.uk/ In ‘Course choice’ tab: Type of Study - ‘Postgraduate Research
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their operational reliability. The PhD student will combine mathematical models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify
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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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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