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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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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