57 systems-science "https:" "https:" "https:" "https:" PhD positions at Forschungszentrum Jülich in Germany
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related field Great interest in energy technology, energy economics and policy issues Experience in programming with Python or a comparable programming language Experience in energy system modelling is an
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required (at least B2 level according to the CEFR: https://go.fzj.de/languagerequirements ) Our Offer: We work on the very latest issues that impact our society and are offering you the chance to actively
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14 Mar 2026 Job Information Organisation/Company Forschungszentrum Jülich Research Field All Researcher Profile First Stage Researcher (R1) Application Deadline 30 Apr 2026 - 22:00 (UTC) Country
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addition to exciting tasks and the collaborative working atmosphere at Forschungszentrum Jülich, we have a lot more to offer ( https://www.fz-juelich.de/en/careers/julich-as-an-employer/benefits ). The position is for
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engineering laboratory facility. Your task will include: Construction and commissioning of the new reaction engineering plant Development and synthesis of inductively heatable, heterogeneous catalysts Operating
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manufacturing in resource strategies and system models for evaluating long-term transformation paths Your Profile: Excellent master`s degree in engineering, materials science, industrial engineering, energy
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website (pay scale table on page 69 and following of the PDF download): https://go.fzj.de/bmi.tvoed FIXED-TERM: The position is limited to 3 years In addition to exciting tasks and a collegial working
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journals Your Profile: A Master’s degree (or equivalent) in chemistry, physics, meteorology, atmospheric sciences, environmental sciences, or related fields Strong interest in experimental atmospheric
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electrical or mechanical engineering Strong mathematical skills Experience in modelling energy systems Very good knowledge and experience in programming (e.g. Python, Matlab, C, C++) Fluent in written and
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, data science, applied mathematics, physics, materials science, or a related field. Solid background in machine learning and/or computer vision. Interest in representation learning, active learning