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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, Dr forest, or Dr rer nat (doctoral studies with a focus on natural sciences) Course location Göttingen Teaching language German English Languages Courses are usually held in English. The
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the umbrella of the Dresden International PhD Programme (DIPP) Teaching language English Languages All courses are held in English. The primary language in the labs is English. The PhD thesis is written and
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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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German Cancer Research Center (DKFZ) Heidelberg • | Heidelberg, Baden W rttemberg | Germany | 1 day 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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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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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