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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
Area of research: Scientific / postdoctoral posts Job description: Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean" (d/m/f) (HIPP26 #3) Background
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for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The purpose of this position is to conduct research in the field of model
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assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 28 days ago
. The Research Atmospheric clouds and aerosols are major sources of uncertainty in weather and climate prediction due to their complex, multi-scale nature — from nanometers to hundreds of kilometers. Turbulence
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Research profile in machine learning (e.g. robustness, out-of-distribution/anomaly detection, fairness, explainability, uncertainty quantification) or AI applications in the healthcare domain Interest in
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LBM implementation. Given that numerous measurements and parameters are subject to uncertainties, the project also incorporates uncertainty quantification (UQ) with the ultimate goal of providing
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. Moreover, the successful candidate will also need to develop a system to estimate the uncertainty of the predictions. Potential solutions could include ensemble generation, a combination of EOF
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methods and their implementation, High-Performance Computing experience, methods of Uncertainty Quantification Offer – What you can look forward to: A dynamic, international team from various disciplines
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
control of such systems, taking particularly into account model uncertainties as well as limitations pertaining to acquisition of data, communication, and computation. We apply our methods mainly to human
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application to the European mission of a Digital Twin Earth. ML research directions will include physics-aware machine learning, reasoning, uncertainty estimation, Explainable AI, Sparse Labels and