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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 2 months ago
Job Offer from July 08, 2025 The Max Planck Society is Germany's premier research organization. The currently 84 Max Planck Institutes and facilities conduct basic research in the service
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that much of the Southern Ocean is, theoretically, open to all nations and has no national-level governance. The region is currently governed through the international Antarctic Treaty System (ATS), with
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to your scientific expertise to work within the German Research Foundation (DFG) funded project “Multi-channel transcranial current stimulation (mc-tCS): a novel approach to modulate smooth pursuit eye
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considered. The ideal candidate will have a background in immunology, tissue biology or cellular metabolism. Experience in working with animal models, cell isolations from tissues (lung, intestine), human
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Excellent skills in statistical modelling, preferably using R Proven track record of publishing in international peer-reviewed journals as first author Willingness to conduct fieldwork and participate in
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PostDoc in "Sustaining the keystone: Rethinking Antarctic krill fishery management under climate ...
National Jurisdiction (ABNJ). This means that much of the Southern Ocean is, theoretically, open to all nations and has no national-level governance. The region is currently governed through
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Postdoc in "Navigating uncertainty: Planning marine protected areas in a changing Southern Ocean"...
Jurisdiction (ABNJ). This means that much of the Southern Ocean is, theoretically, open to all nations and has no national-level governance. The region is currently governed through the international Antarctic
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Your Job: Synthesis and physicochemical characterization of energy materials or representative model systems Characterization of the dynamical and structural properties of energy materials with a
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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