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Max Planck Institute for Intelligent Systems, Tübingen, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
newly developed state of the art statistical model(s) for quadrupeds, and the physical word. We are collaborating with the Swedish University of Agricultural Sciences and National Research Council
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Your Job: In this position, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following
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, helping to shape future research infrastructure Present your results on conferences in Germany and abroad Your Profile: Excellent Master’s degree in statistics, physics, mathematics, or a related
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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Empirical Microeconomics / Applied Microeconomics The initial appointment will be 2 years with the possibility of extension. Salary is in accordance with the German State Regulated Public Salary Scale
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Microsystems and Solid State Technologies EMFT is the ideal place for your career! With around 200 highly qualified employees at our locations in Munich, Oberpfaffenhofen, and Regensburg, we combine many years
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integrate into our databases) integrate living synthesis pipelines into human-in-the-loop processes (e.g. Cochrane crowd or NACSOS ) evaluate and develop automation tools for information extraction help us
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looking for a topic for a Master`s thesis. You are interested in and ideally have previous knowledge of the fields of electron microscopy and solid state physics. You can work independently and responsibly
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science research institute and member of the Leibniz Association, focusing on Africa, Asia, Latin America, and the Middle East, and on cross-regional and global issues. Funded by the City State of Hamburg
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, extending them with physics-based approaches, and adapting existing physics-integrated neural network approaches for stress prediction in arterial walls and plaque. Another part of the project is exploring