33 Mathematics Postdoctoral research jobs at GRK2376 "Confinement Controlled Chemistry" in germany
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cognitive science and computational neuroscience to solve challenging scientific problems Strong mathematical and analytical skills Very good command of the English language For postdoctoral researchers: PhD
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Max Planck Institute for Solar System Research, Göttingen | Gottingen, Niedersachsen | Germany | 7 days ago
and presentation at conferences You should have PhD in fluid dynamics, physics, or applied mathematics with a strong computational background. Experience in development of MPI code and in running on big
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plant and mathematical modeling to determine the benefit of root phenotypes for stress tolerance. Report your work in scientific journals and oral presentations. Your qualifications and skills: You hold a
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collaborative team in an outstanding scientific environment. Qualifications The successful applicant (m/f/d) will hold a Ph.D. in computer science, machine learning, mathematics, bioinformatics or related fields
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of Physics, Mathematics, and Biology of the University, and non-university research institutions in Göttingen, such as the German Primate Center and the Max Planck Institutes for Multidisciplinary Sciences and
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and 5) development of medical devices. The EKFZ relies on a close interdisciplinary collaboration between the University Medical Center Göttingen, the faculties of Physics, Mathematics, and Biology of
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imaging, rhizotrons, and microscopy. Plant and root physiological measurements to determine the function of (interacting) root phenotypes. Functional-structural plant and mathematical modeling to determine
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to cells. Naturally, this research is performed in a highly interdisciplinary manner and involves physics, physical and bio chemistry, cell biology and mathematical data analysis. The RTG 2756 offers a
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masters degree and subsequent Ph.D. degree in Computer Science, Mathematics, Physics Engineering or in a similar field. Alternatively an excellent masters degree with professional experience Very good
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with mathematical modeling and machine learning methods will ultimately allow us to predict the entire recognition space for any given TCR sequence. Our work is embedded into close collaborations with