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cardiovascular simulations, especially with regard to the estimation of biomarkers for image-based diagnostics. The project is a collaboration between WIAS (Research Group “Numerical Mathematics and Scientific
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methods to quantify the propagation of domain uncertainties in cardiovascular simulations, especially with regard to the estimation of biomarkers for image-based diagnostics. The project is a collaboration
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University of Duisburg-Essen, Department of Mathematics Position ID: 1988 -ESAGAPOSTDOC0925 [#26703] Position Title: Position Type: Postdoctoral Position Location: Essen, NRW 45117, Germany Subject
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a methodological focus on applied microeconomic theory, numerical simulations or empirical methods Good understanding of one or more of the following topics: electricity markets, industry
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of in vitro and in vivo pre-clinical models, including hiPSC-derived systems The postdoctoral project will combine experimental (wet-lab) and computational (dry lab) approaches Be part of Geman Center
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Work group: Gerics - Climate Service Center Area of research: Scientific / postdoctoral posts Starting date: 16.06.2025 Job description: PostDoc Position - Resilience enhancement through NBS
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Technische Universität München, Physik-Department T30f Position ID: TUM -Physik-Department T30f -POSTDOC [#27987] Position Title: Position Type: Postdoctoral Position Location: Garching, Bayern
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responsibilities: PIK is seeking a qualified junior researcher or postdoctoral scientist responsible for the estimation of future fire risk with the use of the updated SPITFIRE model, based on future projections of
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Simulations with classical and AI tools to investigate the impact of changes in external forcings on climate extremes during the past centuries, and how this impact changes with increasing greenhouse gas
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Max Planck Institute for Multidisciplinary Sciences, Göttingen | Gottingen, Niedersachsen | Germany | about 2 months ago
the structure from such data is challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine