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Field
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to 1) Object-attribute compositionality to replace exhaustive data requirements with structured concept learning, 2) Bias detection and machine unlearning to identify and mitigate bias and shortcuts
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equations to simulate pollutant transport, mixing and biochemical processes. To enable rapid prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained
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characterize the spatio-temporal contexts that favor crises. • Development of advanced predictive models (multivariate approaches, machine learning) combining event data, snow and weather data, and remote
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communications theory methods Applying optimization techniques and machine learning/AI approaches Conducting simulations and experimental validations Collaborating with Ericsson and Chalmers researchers Publishing
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speed - Provide human experts with a reliable second opinion This project integrates image processing, data analytics, machine learning, and computational modelling, with applications in aerospace
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prediction, a machine-learning surrogate model based on Gaussian process regression will be developed and trained using datasets generated by the high-fidelity numerical solver. The surrogate will emulate key
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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machine learning for time series, geospatial data or dynamic models; ideally experience with deep learning frameworks (e.g., PyTorch). Strong analytical and conceptual skills for designing and interpreting
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aspects of machine learning. Applications include improving the efficiency of data assimilation methods and understanding why and how deep learning works. Applicants should have, or expect to achieve
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candidate in the exciting area of multiscale and multiphysics modelling of sustainable fibrous composites, with additional focus on uncertainty quantification and machine learning. Information The context