181 modelling-and-simulation-of-combustion-postdoc Postdoctoral research jobs in Germany
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- Leibniz Institute for Neurobiology
- Max Planck Institute for Astronomy, Arua, Uganda
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- Max Planck Institute for Brain Research, Frankfurt am Main
- Max Planck Institute for Demographic Research (MPIDR)
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- Max Planck Institute for Plasma Physics (Greifswald), Greifswald
- Max Planck Institute for Radio Astronomy, Bonn
- Max Planck Institute for Sustainable Materials GmbH, Düsseldorf
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Ruhr University Bochum
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- University of Bonn
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management. Our group combines empirical work (with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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(including the doctoral dissertation) Strong methodological training in quantitative survey and experimental research (Additional asset: experience with using large language models in surveys) Proficiency in
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(with experiments in the field and in the lab) and modelling techniques. The focus of this postdoctoral position is the generation of empirical datasets for livestock systems in East Africa, and in
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zusammen mit insgesamt elf Postdoc WissenschaftlerInnen aktiv am wissenschaftlich-akademischen Diskurs zur aktuellen Forschungs- und Entwicklungsfragen im Bereich des Grünen Wasserstoffs teilnehmen. Von
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' prognosis or treatment decisions. For modeling, we use both public and proprietary clinical and research data greatly enriched by our own repository of digital pathology images. A further focus lies on
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at local, regional, national and European scales across the terrestrial, freshwater and marine realms. For this, a suite of thematic VREs will be developed that allow an easy-to-use application of models
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mobility systems through practical and laboratory tests as well as sophisticated simulations. We not only publish research results gained at numerous conferences and in journals, but also make our software
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focus on deep networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning
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communication system are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random