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welcomes applications from individuals with a master’s degree in the following fields: Computer Science Mathematics Natural or engineering sciences or an equivalent degree, interested in High-Performance
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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Research Center (CRC) “Data-driven agile planning for responsible mobility” (AgiMo), funded by the German Research Foundation (DFG). This interdisciplinary center, involving four universities and the German
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Training Group "AirMetro - Technological & Operational Integration of Highly Automated Air Transport in Urban Areas" (RTG 2947) , funded by the German Research Foundation (DFG). This interdisciplinary group
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computational and theoretical physics/chemistry. Capability of team work is essential. Skills in materials chemistry, magnetism, theoretical chemistry, high-performance computing, and programming are beneficial
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, you will develop highly accurate 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
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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spatiotemporal coupling (STC) at the far-field Design optical elements to generate a specific RDG and STC, e.g. doublet lens including axi-parabola Perform laser wakefield acceleration using "flying focus
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. immunohistochemistry, flow cytometry, high-performance liquid chromatography, real-time PCR) Preparation of scientific publications and presentations at conferences and in expert committees in conjunction with business
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industry. The center offers an excellent working environment with access to state-of-the-art technologies and an outstanding high-performance computing infrastructure. For TUD diversity is an essential