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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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are being developed that provide AI-supported tools to identify suitable sources and optimize utilization decisions throughout the product life cycle. Various machine learning approaches are to be used
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These are positions for Doctoral Students, based in Tübingen in an interdisciplinary research group working at the interface of Machine Learning, Medicine, and Biology. Doctoral Students will engage
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27.10.2025 Application deadline: 30.11.2025 Are you excited about the possibility to explore ethical, philosophical, legal, epistemic or social implications of using machine learning in different
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Postdoc (f/m/d) Leader of Junior Research Group "WEEE-Recycling" / Completed university studies (...
-hand experience in the application of machine learning, simulation and modelling concepts in resource technology # Proven track record of interdisciplinary collaboration along the value chain of raw
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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, agricultural sciences with a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a
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include: Item response theory, causal inference in non-experimental designs, psychometrics, randomized controlled field trials, longitudinal and multilevel modelling, machine learning methods, artificial
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analysis (e.g., econometrics, statistics, machine learning) A high motivation and the ability to work independently with a strong team orientation Excellent spoken and written English and the will
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Max Planck Institute for Astrophysics, Garching | Garching an der Alz, Bayern | Germany | 7 days ago
based on a combination of novel simulation techniques, Bayesian statistical methods and machine learning approaches. The successful candidate will work closely with Prof. Dr. Volker Springel, the director