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, ensemble Kalman filters, and physics-informed neural networks (PINNs) enforce conservation laws while fitting observations. The key is to apply the vast amount of physical insights developed in turbulence
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implemented within an ensemble variational data assimilation system, enabling short-term forecasts based on sea ice concentration and thickness data while providing associated uncertainty estimates. In a second
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predicting pollutant dispersion in complex environments like industrial sites remains difficult due to fluctuating wind conditions and obstacles. This PhD project offers a unique opportunity to develop
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traitement avec un minimum d'effets secondaires, en tenant compte des profils métaboliques génotypiques et phénotypiques individuels. L'état du métabolisme cellulaire est évalué par un ensemble de différents
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., feature engineering, spatiotemporal modeling, Bayesian calibration, ensemble methods) to improve prediction accuracy and uncertainty quantification. Disseminate research findings through presentations
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, including renewable energy sources and energy storage systems.Development of predictive models and soft sensors for monitoring the technical condition and operational parameters of energy infrastructure (e.g
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of conflict prediction. Lead the design and improvement of ensemble routines and validation protocols in the VIEWS forecasting system. Publish research findings in top peer-reviewed journals and contribute
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Position Summary The Dickson, Feig, Vermaas, Wei, Woldring laboratories together form Team Green, a collaborative research effort funded by DARPA to predict protein structural ensembles, ligand
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/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g., SHAP, LIME) and radiomics preferred
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Expertise: Familiarity with supervised/unsupervised learning (regression, classification, clustering), ensemble methods, and deep learning architectures (CNNs, RNNs). Experience with explainable AI (e.g