173 computer-programmer-"https:"-"Inserm" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" positions at Leibniz
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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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our online application portal for this job posting, reference number 2026-SY-1, at https://www.atb-potsdam.de/en/career/vacancies . Applications received after the application deadline cannot be
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by the University of Hamburg. All further information on the application process and contacts can be found here: https://www.uni-hamburg.de/en/stellenangebote/ausschreibung.html?jobID
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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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opportunities in research methods and academic publishing. Doctoral candidates at the GWZO participate in the program of the Integrated Research Training Group of the Graduate School Global and Area Studies
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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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, annual bonus, company pension plan with the Versorgungsanstalt des Bundes und der Länder (VBL). In-house support for Fellowship applications. Work-life balance (certified by Audit Beruf und Familie) as
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of record, motivation letter) by March 8th, 2026. Please send it via our online applications system (single pdf file, less then 5 MB) https://www.leibniz-inm.de/en/job-offers-2/ For further information
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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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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