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Technische Universität München, Physik-Department T30f Position ID: TUM -Physik-Department T30f -POSTDOC [#27987] Position Title: Position Type: Postdoctoral Position Location: Garching, Bayern
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coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
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international researchers and industrial partners. A unique opportunity to research next-generation batteries. Strong support to perform high-quality research and to present and publish your research results
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coding experience with e.g. Python/Matlab/R Practical experience with High Performance Computing, and scientific programming and a willingness to learn to work with high-performing computing systems
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Helmholtz Association. Its qualification schemes for young researchers are geared mainly towards PhD students, postdocs and young managers. The Helmholtz Association has set high standards for its talent
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, preferably in Python, Fortran, Matlab or R #experience in the environment of High Performance Computing (HPC) is desirable, but not mandatory #capability to work in a team but able to formulate and carry out
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Corporate Health Management Program offers a holistic approach to your well-being Develop your full potential: access to the DKFZ International Postdoc Program and DKFZ Career Service with targeted offers
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
using first-principles simulations software (density functional theory and related codes) # Automated Workflows:Utilize automated workflows on high-performance computing systems for efficient data
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-Scripting very good knowledge in programming, preferably in Python,Fortran, Matlab or R experience in the environment of High Performance Computing (HPC) is desirable, but not mandatory capability to work in
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Tübingen offers a combination of high-performance medicine and strong research. The goal of the Carl-Zeiss-Project “Certification and Foundations of Safe Machine Learning Systems in Healthcare” is to enable