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Privacy-Preserving and Reliable Artificial Intelligence to be occupied starting from the 01.10.2022. The Trustworthy and Privacy-Preserving AI Group (PI: PD Dr. Georgios Kaissis) at the Institute for AI in
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of hydrological models to reliably forecast extreme hydrological events. However, these data sets must initially undergo interpolation before integration into hydrological models. Current research is developing
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Description Reliable monitoring and control of water systems is essential to protect water resources, ensure hygienic standards, and enable sustainable infrastructure operation. As challenges evolve
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separately, yet a reliable, open-source tool integrating a shallow-water solver and a multiphase porous-media solver within the same framework is missing. Without this coupling, it is not possible to predict
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. The aim is to evaluate and improve the long-term reliability of these materials for green hydrogen production. To achieve this, your responsibilities will include: Design and execution of experiments
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, please contact Dr. Robert Georgii (robert.georgii@frm2.tum.de) or Prof. Dr. Skyler Degenkolb (degenkolb@physi.uni-heidelberg.de). The high safety standard of our facility requires the reliability
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theory. While the GW method reliably describes photoelectric (main) peaks, it often fails to accurately capture satellite positions, which require more advanced techniques such as the cumulant approach (GW
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computational tools for predicting satellite features in XPS spectra of 2D framework materials. Your work will be based on the GW approximation within Green’s function theory. While the GW method reliably
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in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content The Konrad Zuse School of Excellence in reliable AI (relAI) offers fully funded
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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough