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the application which project you are specifically interested in. Further details on the projects can be found here: https://www.fz-juelich.de/en/jcns/careers/fellowships/tasso-springer-fellowship-program Your
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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the INW-1 machine learning team on data handling, online analysis, design of experiments (DoE), and data categorization to enable efficient and automated evaluation of operando experiments Collaboration
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30 days of vacation plus additional days off (e.g. between Christmas and New Year`s) FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time
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Systems, Tübingen/Stuttgart in Germany. Together we want to build a lighthouse for machine learning and modern artificial intelligence in Europe. The cooperation spans all levels, from leading experts
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English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally supported by a certificate confirming the language level. Knowledge of German is not prerequisite
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the development and application of probabilistic inference methods and machine learning techniques for quantitative uncertainty modeling and for the integration of heterogeneous climate data
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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hierarchies You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ), ideally supported by a certificate confirming the
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user of supercomputers and sufficient programming skills You have a very good command of written and spoken English (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements