107 computer-programmer-"https:"-"Prof" "https:" "https:" "https:" "https:" "UNIV" positions at Leibniz
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, or comparable research contributions) Experience analyzing structure–property relationships in polymer and composite systems Ability to independently plan experiments and document results Strong written and oral
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plan (VBL) Flexible, family-friendly working conditions Good transportation connection with parking facilities Restaurants and cafeterias in close proximity Central location near the city center You can
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- Integrating exudation into the root economics space to better understand carbon and nutrient cycling in managed grasslands” is part of the DFG Priority Program 1374 “Biodiversity Exploratories”. The project
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Individual development plans, mentoring programs, and support from experienced specialists Open, team-oriented work atmosphere in an international environment 30 days of annual leave Company pension plan (VBL
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cooperate with the University of Rostock and are an integral part of the teaching program of the Institute of Physics. Our network includes the research community worldwide. As an institute of the Leibniz
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programme Generous support for your pension provision as a direct insurance policy Promotion of your skills through further training measures Contact For further information concerning the tasks please
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at leveraging graph-theoretic approaches to analyze and predict food-effector
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of family and career Excellent conditions for developing a scientific career, including: in-depth knowledge in the fields of liver research and toxicology (Examples of our publications: https://doi.org
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genomics will be used to address questions relating to evolutionary relationships and biomineralization in Annelida. This position is funded through the Leibniz Collaborative Excellence Programme, with
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in