170 computer-programmer-"https:" "https:" "UNIS" "https:" "https:" "https:" "https:" "UCL" positions at Leibniz
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(TIB ) – Leibniz Information Centre for Science and Technology – Program Area C, Research and Development, is looking to employ a Research Software Engineer for Digital Research Infrastructure (m/f/d
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complete list of publications, one-page motivation letter, at least two letters of reference) by March 15th, 2026. Please use our online application system via https://www.leibniz-inm.de/en/job-offers-2
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preference. Your application: We are looking forward to receiving your online-application (http://www.ipk-gatersleben.de/en/job-offers/) as one single pdf-file by 15.02.2026. If you have questions or require
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Brandenburg. Support data analysis using pre-developed scripts in R Your qualifications: Currently enrolled in a Bachelor’s or Master’s program in Environmental Sciences, Engineering, Agriculture, Horticulture
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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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two letters of reference) by January 31st , 2026. Conveniently use our online application system: https://www.leibniz-inm.de/stellenangebote/ The INM is an equal-opportunity employer with a certified
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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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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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The LIT - Leibniz Institute for Immunotherapy (foundation under civil law) (https://lit.eu/ ) formerly RCI – is a biomedical research center focusing on translational immunology in the fields
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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