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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early diagnosis, and therapy of diseases like cardiovascular diseases or cancer. Overall, the institute strives...
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The DWI – Leibniz-Institute for Interactive Materials is a federal and state funded research institute of the Leibniz Association based on the Melaten Campus of RWTH Aachen University. International young and experienced scientists from various disciplines develop interactive materials based on...
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Master’s Student (m/f/d) – Thesis The Leibniz Institute for Food Systems Biology at the Technical University of Munich (LeibnizLSB@TUM), a legal foundation under civil law based in Freising, is a prominent member of the Leibniz Association. Our institute integrates cutting-edge biomolecular...
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early diagnosis, and therapy of diseases like cardiovascular diseases or cancer. Overall, the institute strives...
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: Mathematical modelling will be used to investigate the processes and consequences of disease progression from liver fibrosis to cirrhosis to hepatocellular carcinoma (primary liver cancer), to select specific
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The German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE) is a member of the Leibniz Association. The institute’s mission is to conduct experimental and clinical research in the field of nutrition and health, with the aim of understanding the molecular basis of nutrition-dependent...
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The Leibniz Centre for Tropical Marine Research (ZMT) GmbH (www.leibniz-zmt.de ) is an independent research and teaching institute that provides scientific knowledge for the protection and sustainable use of tropical coastal ecosystems. To this end, we work in an inter- and transdisciplinary...
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defined requirements for estimating CO₂ concentrations in surface waters. Requirements: Doctoral degree in mathematics, physics, or equivalent numerical disciplines Background in marine sciences Experience
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qualifications Successfully completed university degree (master, diploma, or equivalent) in computer science, physics, engineering, mathematics, or comparable field, In-depth theoretical knowledge in Machine
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machine learning tools on the experimental and numerical data. Requirements Master degree in physics, chemistry, mathematics or related fields Knowledge in materials science Knowledge of python good