157 modelling-and-simulation-of-combustion-postdoc Postdoctoral research jobs in Germany
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- Technical University of Munich
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- ; Queen Mary University of London
- European Magnetism Association EMA
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- Helmholtz-Zentrum Geesthacht
- Humboldt-Universität zu Berlin
- Leibniz Institute for Neurobiology
- Max Planck Institute for Biology Tübingen, Tübingen
- Max Planck Institute for Brain Research, Frankfurt am Main
- Max Planck Institute for Chemical Physics of Solids, Dresden
- Max Planck Institute for Extraterrestrial Physics, Garching
- Max Planck Institute for Human Development, Berlin
- Max Planck Institute for Molecular Biomedicine, Münster
- Max Planck Institute for Nuclear Physics, Heidelberg
- Max Planck Institute for Physics, Garching
- Max Planck Institute for Plasma Physics (Garching), Garching
- Max Planck Institute for Plasma Physics (Greifswald), Greifswald
- Max Planck Institute for Sustainable Materials GmbH, Düsseldorf
- Max Planck Institute for the Structure and Dynamics of Matter, Hamburg
- Max Planck Institute for the Study of Crime, Security and Law, Freiburg
- Technische Universität München
- University of Duisburg-Essen
- University of Greifswald
- University of Paderborn
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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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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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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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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
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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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of immune cells and in functional assays determining T cell functions - Experience with in vivo experiments in preclinical model systems of viral infection - Excellent writing and communication skills