53 postdoc-computational-fluid-dynamics PhD positions at Technical University of Munich
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16.08.2023, Wissenschaftliches Personal The Chair of Computational Modeling and Simulation (CMS) at the Technical University of Munich invites applications for the position of a Research Assistant
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Master’s students, supporting their academic and research development. ▪ You will be part of a dynamic international team dedicated to advancing quantum technologies and computing with superconducting qubits
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and Master’s students in Informatics and Data Science. Supervise Bachelor’s and Master’s theses. We Offer Practice-oriented research projects with leading academic and industry partners (like Google
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facilities (sequencing, proteomics and metabolomics). This dynamic setting fosters innovation and teamwork, enabling cutting-edge discoveries in the field of microbiome studies. The candidate will work on a
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408. The Technical University of Munich (TUM), as a University of Excellence, is one of the leading and most dynamic research institutions in the country. TUM has established the Collaborative Research
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to the computational complexity of climate models, these will be replaced by physics-informed deep learning surrogates in the aforementioned model coupling. The project will initially focus on one main application
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or computational science Strong mathematical skills and interest in developing new mathematical methods Good knowledge of mathematical/numerical optimization methods or deep learning methods Enthusiasm
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(https://soilsystems.net/ ), a Priority Programme (SPP 2322) funded by the Deutsche Forschungsgemeinschaft (DFG; German Research Foundation). Within SoilSystems, scientists from different disciplines from
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. Candidate´s profile: • Master’s degree in Life Sciences, in Computational Biology or MD degree • Previous research experience in immunology • Experience in flow cytometry, cell culture and in high-dimensional
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messengers transported by the flow or even the pressure of the fluid itself. In an interdisciplinary team, you will either develop theoretical models of the feedback between flow and network architecture