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Field
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design and characterise reconstruction methods—both machine-learning-based and traditional—for the bundles of muons that reach the detectors, and apply them to data and simulations to constrain cosmic-ray
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Start date – 1st March 2027 (latest) Project Description Machining generates large quantities of swarf, with many components losing 60–90% of their material during the machining stage. This swarf
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University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners. Key
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This exciting opportunity is based within the Power Electronics and Machines Control Research Institute of the Faculty of Engineering at the University of Nottingham which conducts cutting edge
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to achieve them. Acquire new knowledge quickly and use existing knowledge in new ways. Work constructively under pressure or in the face of adversity. Demonstrate strong problem-solving abilities with a
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made significant progress in this direction by merging machine learning interatomic potentials (MLIPs) trained on density functional theory (DFT) data, and enhanced sampling techniques to reach the
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language processing, large language models, machine learning, network analysis, social media analytics, and large-scale analysis of online discourse and communities. This PhD scholarship will be based within
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electrical engineering, control engineering, applied mathematics, computer science, or a related field A strong background in probability and statistics, machine learning, or control theory Interest in cyber
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candidate for a full-time (100%) PhD position for 3 years. You will join the research group Power Electronics and Electrical Machines (PEM) at IEL, where we foster an open, inclusive, and collaborative
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, engineered powders, including Cermetal and WC-Co-based materials, will be investigated as energy-absorbing media within the damping system. The development combines computational modeling, machine learning