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Max Planck Institute for Evolutionary Anthropology, Leipzig | Leipzig, Sachsen | Germany | 3 days ago
Job Code: CF-06-2025 Job Offer from November 12, 2025 The Max Planck Institute for Evolutionary Anthropology (MPI EVA) brings together scientists from diverse backgrounds (natural sciences and
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well as cooperation of science and economy. GSaME offers the optimal conditions for graduates to earn the titles of Dr-Ing and Dr rer pol. Young scientists have the opportunity to be integrated
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scientists. You will contribute to the top scientific experiments, will obtain practical experience, improve your soft skills (presentation, communication, language, etc), learn the strategies of data
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of molecular and biological matter using X-ray and neutron scattering. One of the research areas is the development of machine learning (ML) based approaches to efficient analysis of the vast data amounts
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automated laboratory equipment to ensure reproducible and data-rich experiments. Close collaboration with chemists, engineers, and data scientists will enable the creation of a robust, high-throughput
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experiments. Close collaboration with chemists, engineers, and data scientists will enable the creation of a robust, high-throughput framework for future catalyst and layer development and efficient
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for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute to the studies of protein
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different and specific medical disciplines. The projects are mentored by teams consisting of a scientist and pharmacists as well as clinicians. If applicable, at the beginning of the project, every new
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machine learning tools for the efficient analysis of the experimental data. For more information, visit our web page www.soft-matter.uni-tuebingen.de We are looking for a motivated PhD student to contribute
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-funded project with 23 partner institutions, that aims to create a robust monitoring network to: support the expansion, improvement and harmonization of existing data, knowledge, and monitoring approaches