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Field
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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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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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selection criteria Knowledge/experience with control engineering, information fusion and/or data assimilation, marine technology Knowledge of and hands-on experience with machine learning and/or statistical
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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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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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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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" "Machine-learning-based imaging processing" webpage For further details or alternative opportunities, please contact: [email protected].
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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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, 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
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the PhD to develop skills in areas such as programming, data analysis, machine learning and signal processing. This will provide the technical foundation required to work with large acoustic datasets and