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skills. More information may be found on the ENERPOL home page: https://cordis.europa.eu/project/id/101226229 The position provides salary according to TV-L 13, which is in accordance with EU guidelines
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establishing the institute Writing of third-party funding grants and support thereof Your Profile: Master`s degree with PhD in physics, chemistry, materials science, chemical engineering, or a related discipline
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continuum within the 2nd phase of CLICCS (https://www.cliccs.uni-hamburg.de/about-cliccs/cliccs-ll.html). In CLICCS-M4, we are further developing the unique ICON-Coast model within the ICON Earth System
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results in relevant journals and at international conferences Your Profile: A completed university degree (Master or equivalent) with excellent grades in the field of mechanical engineering, material
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. Programme duration 6 semesters Beginning Winter semester Tuition fees per semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content
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: Completed master's degree in computer science, mechatronics, or electrical engineering. Strong programming skills (C/C++, Python; hardware description languages such as HLS or VHDL are an advantage
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degree (Master) in Computer Science or related fields Research interests in one or more of the following topics: knowledge graphs, information extraction, machine learning or artificial intelligence
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Max Planck Institute for Demographic Research, Rostock | Rostock, Mecklenburg Vorpommern | Germany | 27 days ago
found at https://www.imprs-phds.mpg.de. How to apply Applications must be submitted online via this survey by April 15, 2026, 23:59 CET and include as a single PDF file, in English: Curriculum Vitae
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, Molecular Biology, Chemistry, Biotechnology, Biophysics or related for the preparation of a Master's thesis for a duration of 6 months. The topic of the thesis is “Optimization of DNA Synthesis via
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research group (please find further information at https://www.biologie.uni-konstanz.de/gruber ). You will be developing machine learning-based data science approaches for the analysis of Next Generation