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information of TUM. Kontakt: contmech@mw.tum.de More Information https://www.epc.ed.tum.de/ddmm/aktuelles/article/phd-position-in-collaboration-with-bmw-on-data-driven-modeling-of-structural-foams-for-high
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close to full-time, allow you to tailor your working hours to suit your individual needs. For more information, visit: https://go.fzj.de/near-full-time WORK-LIFE BALANCE: Flexible working arrangements
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Leipzig, Sachsen | Germany | 2 months ago
Additional Information Website for additional job details https://jobs.helmholtz-muenchen.de/jobposting/553ccda55fc7aad0ef9bdfa43d242fb54… Work Location(s) Number of offers available1Company/InstituteHelmholtz
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and other stakeholders - knowledge-driven and application-inspired. FAIRagro is a consortium within the National Research Data Infrastructure (NFDI) in Germany. Its goals and activities focus
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). The institute also maintains locations in Dedelow and Paulinenaue. The Research Data Management Working Group at ZALF is looking for a research assistant for data management tasks within the framework
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request one if your application is successful. For further information, please visit the website: https://www.kmk.org/zab/central-office-for-foreign-education.html For further information or to discuss the
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data for automatic documentation of a worksite within the Marie Skłodowska-Curie Industrial Doctorate (ENGAGE Network). ENGAGE Network PhD Position Mobile working machines (MWM) are critical
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opportunities. Severely disabled people will be given preference if equally qualified. You can find more information about our institution on the Internet at: https://leibniz-lib.de/
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, current version available at https://recht.saarland.de/bssl/document/jlr-HSchulGSLrahmen ). LanguagesGERMANLevelExcellent LanguagesENGLISHLevelExcellent Additional Information Benefits Saarland University
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the characterization of these components on 300mm silicon wafers, large amounts of data are generated by extensive electrical measurements. An efficient preparation of data is therefore crucial for short learning cycles