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., probability, analysis), eager to conduct cutting edge research in the field of uncertainty quantification, in particular the theory and methods known as predictive Bayes. Predictive Bayes theory involves
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for simulation and modeling of wave dynamics, and for uncertainty quantification of extreme events. The project will combine stochastic mathematical models of wave physics with advanced computational methods
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better subsurface understanding, uncertainty quantification, and robust forecasts suitable for emissions reduction, increased energy efficiency, and recovery improvements with large amounts of data
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view towards developing new methods for uncertainty quantification. Starting date no later than October 1st, 2026. The fellowship period is four years, with 25 percent of the workload being devoted
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statistics. We are looking for a motivated candidate, with a deep interest in mathematical statistics, with a view towards developing new methods for uncertainty quantification. Starting date no later than
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for simulation and modeling of wave dynamics, and for uncertainty quantification of extreme events. The project will combine stochastic mathematical models of wave physics with advanced computational methods
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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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multimodal omics data. The project will focus on multimodal representation learning, uncertainty quantification, and interpretable and biologically plausible modeling, with the goal of building foundational AI
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human and natural systems as well as intrinsic variability. The need to translate to variables and scales relevant for stakeholders with appropriate uncertainty quantification requires physics-guided AI
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progression modelling, exploiting advances in deep feature learning and uncertainty quantification to support the Bayesian framework, as well as implementation of computational models of neurodegeneration