561 postdoctoral-position-in-molecular-dynamic-simulation-self-assemble-polymer PhD positions in Germany
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Description TUD Dresden University of Technology, as a University of Excellence, is one of the leading and most dynamic research institutions in the country. For TUD diversity is an essential
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Universität Düsseldorf, Physics Position ID: Universität Düsseldorf -Physics -PD [#28569] Position Title: Position Type: Postdoctoral Position Location: Düsseldorf, Nordrhein-Westfalen 40225
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field of bioinorganic spectroscopy, with a particular emphasis on electron paramagnetic resonance (EPR) spectroscopy. This position is part of a newly established research group (2025) in the Department
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offer: We are a young and dynamic group with a strong focus on collaboration and mutual inspiration. The laboratory has state of the art biochemistry and molecular biology equipment. Additionally
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! RESPONSIBILITIES: You will elucidate the molecular mechanisms driving the development of distinct malignant lymphoma subtypes and contribute to the identification of predictive biomarkers and novel therapeutic
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state of Rhineland-Palatinate. Our research focuses on the biology of the cell nucleus and ranges from the molecular level to systems and computational approaches. Researchers at IMB are supported by
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The Department “Genes and Environment” (lead by Prof. Dr. Dr. med. univ. Elisabeth Binder) at the Max Planck Institute of Psychiatry in Munich is offering a position as a PhD Student (m/f/d
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Work group: Institute of Coastal Ocean Dynamics Area of research: Other Part-Time Suitability: The position is suitable for part-time employment. Starting date: 12.06.2025 Job description: PhD
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of great ape communication to identify evolutionary trends and simulate dynamics that might explain the evolution of common ground in humans. For more information see the following representative articles
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Leibniz Institute of Plant Biochemistry (IPB) in Halle (Saale), Germany, where we are offering a fully-funded PhD position within the DFG Priority Programme SPP2363: “Molecular Machine Learning”. About the