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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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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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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
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, creativity and openness to new approaches, a high level of competence in oral and written German and English, reliability, engagement, and a high level of motivation, and capacity for teamwork. We offer
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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journals is desired Ability to work reliably and collaboratively in a scientific research setting Strong command of spoken and written English; German language skills are a plus. We offer Involvement in a
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operating systems Structured and analytical thinking as well as a systematic, careful, independent, and reliable working method Interest in working closely with experimentalists Strong cooperation and
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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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reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be complemented by own lab testing e.g., SSRT incl