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optimization. In this context, interdisciplinary research is strongly encouraged—particularly collaborations spanning computer science, computational linguistics, cognitive science, and the learning sciences
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DIPF | Leibniz Institute for Research and Information in Education contributes to addressing challenges in education through empirical research, digital infrastructure and knowledge transfer. At its
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, biogeochemistry or comparable studies good English speaking and writing skills good knowledge in plant and/or soil ecology basic understanding in chemistry and lab work comprehensive knowledge in statistics and
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Skills/Qualifications Must have, at the start of their PhD programme, a Master (or equivalent) degree in Mechanical Engineering, Physics or Photonics with solid knowledge of optics and its applications
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biology, bioengineering, chemical engineering, or a related discipline Knowledge and experience in the analysis of metagenomics, untargeted metabolomics, or other biological high-throughput datasets
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Solid knowledge of molecular biological methods Experience in executing large-scale evolution experiments Sound knowledge of ecological and evolutionary theories and concepts Practical experience in
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international peer-reviewed journals and presenting them at conferences • Supporting knowledge transfer activities, including policy briefs, workshops, and outreach formats within the NEO consortium Requirements
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-domain context. Good knowledge of programming languages (e.g. Python, R, Java, C#, C++) Good knowledge of object orientation and at least basic knowledge of UML Knowledge in the field of machine learning
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grades (Bachelor and Master: applicants of successive postgraduate-doctoral program submit an education cerificate instead of the graduation certificate) Certificate of knowledge of German or
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. Your profile: Completed Master’s degree or diploma (with excellent to good results) in Computer Science, Software Engineering, Information Systems, or a related field. Strong knowledge of software