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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models with emphasis on: Data-driven practical
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systems (industry, medicine, environment) held by PDEs and/or data-driven, computational fluid dynamics for optimisation and control, data science, automatic learning and uncertainty quantification, offline
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systems (industry, medicine, environment) held by PDEs and/or data-driven, computational fluid dynamics for optimisation and control, data science, automatic learning and uncertainty quantification, offline
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acquired knowledge. Particular emphasis will be placed on uncertainty quantification, longitudinal monitoring, and anomaly detection. The clinical application focuses on longitudinal monitoring of brain
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candidate will be supervised by P.M. Congedo, E. Denimal Goy and Olivier Le Maître, experts in uncertainty quantification methods. The work will be conducted in the Platon team, a joint research group between
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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that requires tightly integrated approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models, with emphasis
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applications using patient-specific data Very strong expertise in the theory and application of Physics Informed Neural Networks to inverse problems Expertise in sensitivity analysis and uncertainty
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high-fidelity simulation environments and Monte Carlo frameworks to validate estimation and tracking algorithms. Perform statistical analysis of algorithm performance, uncertainty quantification, and