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Field
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terrestrial system models, for example using data analysis methods, such as data assimilation, physical- or process-based machine learning, or deep learning algorithms Analysis of the effects of human
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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- “Data-driven algorithms for multi-product inventory systems with non-stationary demand”. Qualifications Applicants should have:- (a) a doctoral degree, preferably in the area of operations management
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to remove PFAS. To accomplish these goals, the candidate will participate in the development of AI/ML algorithms for the prediction of chemical properties, infrared and mass spectra, and ionization cross
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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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cooperative, competitive, and mixed settings. Collaborative decision-making frameworks and decentralized learning algorithms. Adaptive, meta-learning, and context-aware strategies to enhance policy
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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EPSRC-funded project, MAPFSI that will be focused on developing experimentally-validated computational algorithms for fluid-structure interaction problems including multiphysics effect of electromagnetism
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validation of algorithms using these databases, and related data analysis. The ideal candidate will have a strong understanding of SQL and expertise in programming languages such as Python and/or R. Main
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, and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm