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, both written and verbal Knowledge of German and/or a willingness to learn Computer/programming literacy, for example in R, and/or software used in image processing (Adobe Photoshop, ImageJ etc.) Ability
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programs, the university unites the natural and engineering sciences with the humanities, social sciences and medicine. This wide range of disciplines is a special feature, facilitating interdisciplinarity
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oriented, regionally anchored top university, developing innovative solutions for the world's most pressing issues. In research and education, the university unites the natural and engineering sciences with
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skills in one or more languages (Python, C/C++, or others) experience in mechanical testing profound knowledge of machine learning methods (e.g., neural networks, Gaussian processes, active learning
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Degree PhD (Division of Mathematics and Natural Sciences) or, alternatively, Dr rer nat Course location Göttingen In cooperation with Max Planck Institute for Multidisciplinary Sciences (MPI-NAT
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of biological intelligence. World class scientific research work is at the core of the IMPRS-BI mission. We offer an English-speaking, structured PhD programme with ready access to state-of-the-art facilities
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Very good written and spoken English Our offer The full-time position as a PhD researcher (m/f/d) is to be filled with a start in September 2025 or later. Part-time work is generally possible. The
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activities #participation in stakeholder co-design processes, i.e., with regional authorities, nature conservation organizations, farmers - Reporting and Publication #prepare regular progress reports
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German Cancer Research Center (DKFZ) Heidelberg • | Heidelberg, Baden W rttemberg | Germany | about 16 hours ago
in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content The DKFZ International PhD Programme in Cancer Research at the German Cancer
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methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using methods you develop before being transferred to a knowledge graph