672 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" uni jobs in Germany
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/Pages/Datenschutzerklaerung.aspx Where to apply Website https://jobs.zalf.de/jobposting/e15743565213697f9b525b30a89757e8a0ba0d700 Requirements Research FieldAgricultural sciencesEducation LevelPhD
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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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with institutes and working groups at Geisenheim University is a prerequisite. For further information, see https://www.zalf.de/de/struktur/iat . Tasks: We are looking for an internationally renowned
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documents by February 16, 2026 (stamped arrival date of the university central mail service or the time stamp on the email server of TUD applies) preferably via the TUD SecureMail Portal https://securemail.tu
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! This job offer is closely linked to a related opening at IET-3 at Forschungszentrum Jülich within the same project consortium: https://www.fz-juelich.de/en/careers/jobs/2025-362 Please feel free to apply
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., room reservations, invitations), administrative tasks such as documentation and social media posts on project progress The selection is done according to the SCRIPTS Diversity Rules & Guidelines: https
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(including cover letter, CV, diplomas/transcripts, etc.), which you can submit via our online-application-system: https://www.hzdr.de/db/Cms?pNid=490&pLang=en&pOid=76369
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person who is eager to learn more and has a strong interest in research. Experience with membrane filtration systems (of any scale) is a definite advantage, as well as being comfortable in specifying
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programming and data analysis skills (e.g., Java) are an advantage We are looking for first-class graduates with expertise in the RTG-addressed PhD subjects, high interdisciplinary desire to learn and
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parameterization and shows limited adaptability to different vehicle types and operating conditions. The aim of the master's thesis is to investigate an end-to-end reinforcement learning approach for path tracking