22 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" 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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an adaptable Machine Learning (ML) hardware architecture to solve Artificial Intelligence (AI) classification tasks using Internet of Things (IoT) sensor data. This will be a small system-on-chip designed
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reservoirs. By embedding governing equations and boundary conditions directly into machine-learning models, the project aims to enable efficient exploration of high-dimensional parameter spaces without
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established classical protection schemes with data-driven methods, including artificial intelligence and machine learning. The proposed protection strategies are expected to exhibit the following key attributes
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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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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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are seeking ambitious, innovative individuals to join our dynamic team. To find out more about our group and research, visit: https://www.ncl.ac.uk/engineering/research/electrical-electronic-engineering
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models, in-house laboratory tests in a wind-wave-current flume (https://research.ncl.ac.uk/amh/ ) and numerical methodology to quantify biofouling impacts on flow-induced vibration phenomena, structural
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: https://events.teams.microsoft.com/event/376b2195-d8da-47c0-86e2-b18813ec19e3@4a5378f9-29f4-4d3e-be89-669d03ada9d8 . Number Of Awards 1 Start Date 1st October 2026 Award Duration 3.5 years Application
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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