64 structures "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships at Forschungszentrum Jülich
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mentoring for building a career in academia or industry Professional development through JuDocS, including training courses, networking, and structured continuing education ( https://www.fz-juelich.de/en
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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in
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to gain insights into complex systems and inform decision-making Design and evaluate scenarios that focus on energy systems and structural change, assessing their potential impact Develop and evaluate
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): https://go.fzj.de/JuDocs SUCCESSFUL START: It is important to us that you quickly settle into the team and are given structured training for your tasks. We also support you from the very beginning and
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and individually, for example through training opportunities and the structured JuDocS program for doctoral candidates: https://www.fz-juelich.de/en/judocs In addition to exciting tasks and a
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retreats) https://www.hds-lee.de/about/ A qualification that is highly welcome in industry 30 days of annual leave and flexible working arrangements, including partial remote work Further development of your
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graduate school program (including data science courses, soft skill courses and annual retreats) https://www.hds-lee.de/about/ Further development of your personal strengths, e.g., via a comprehensive
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to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so-called lean alloys, and material systems with a high tolerance to impurities from
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development of your personal strengths, e.g., via a comprehensive further training program; a structured program of continuing education and networking opportunities specifically for doctoral researchers via
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heavily relies on empirical determination of key model parameters. By combining protein structure descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange