185 computer-programmer-"https:" "https:" "UNIS" "https:" "https:" "https:" "https:" "UCL" positions at Leibniz in Germany
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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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consequences for essential ecosystem processes. In the framework of the Biodiversity Exploratories (https://www.biodiversity-exploratories.de/en/ ), funded by the German Research Foundation (DFG), the project
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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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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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networks are rewired in response to physiological or pathophysiological stimuli. The project will be conducted jointly with the 'Structural Interactomics Group' led by Prof. Fan Liu (https
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), the largest long-term educational study in Germany. Your work environment ENTAILab is the core infrastructural service and research centre of the “New Data Spaces” program, a DFG-funded multi-local
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, computer science, medicine, pharmacology, and physics. ISAS is a member of the Leibniz Association and is publicly funded by the Federal Republic of Germany and its federal states. At our location in
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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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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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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