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Field
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): · Hybrid Multiscale Modelling · Hybrid Physics-Machine Learning Analyses · Coupled Atmosphere-Turbine Models · Uncertainty Quantification in Hybrid Frameworks We offer the opportunity to work in a very
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. - The individual cannot have held previous positions in the professional ranks. Preferred Qualifications Preference will be given to candidates with expertise in: - Uncertainty quantification and stochastic
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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: Research and tool development at the intersection of artificial intelligence and scientific computing including physics-informed neural networks and digital twins; uncertainty quantification, high
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, visualization, and discovery to prediction, validation, quantification of uncertainty, and inference. The Statistical Scientist will join a highly collaborative research team to accelerate and extend ongoing
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for optimization, history matching, decision making, uncertainty quantification, etc Conduct geothermal assessments, as this is a hot field (> 150 C) The candidate can also suggest other objectives / research
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life sciences — often involving large, heterogeneous datasets and high uncertainty. Two application pillars Life Sciences & Health — From biological data science to health-related applications, IDEAS
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applied to geospatial or geotechnical data, including model training, validation, and uncertainty quantification. Proficiency in programming languages such as Python, with experience in scientific computing
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interactive system design, serious games, or real-time human-machine interfaces. Experience with AI methods such as generative models, reinforcement learning, online/adaptive learning, or uncertainty
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physics, multiscale modeling, and uncertainty quantification. The Multiscale Modeling of Fluid Materials group at the Technical University of Munich is looking for talented and ambitious scientists