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required. Very good English skills, both written and spoken, are also required. The successful applicant has a proven track record of excellent publications. Strong taxonomic knowledge in marine
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interdisciplinary perspectives excellent communication skills in English (both written and oral) a willingness to engage with and learn the programming languages and software tools necessary for the modeling work. We
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and actual work settings. Tasks include: Developing research paradigms Programming experiments and analyzing data Presenting results at conferences and writing research articles Supervising research
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methods and ideas PhD in Physics, meteorology or a related subject Experience with running and evaluating models English skills that allow one to communicate in an international working environment, and to
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, conference contributions, lecture presentations, etc.) in English. Requirements: • A completed degree (Master's) in Applied Mathematics, CSE, or comparable programs with above-average results. • Good
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, the University of Greifswald, the Leibniz Institute of Atmospheric Physics (IAP), and the German Aerospace Center (DLR) Who are we? The IOW is an independent research institute of the Leibniz Association for which
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“Research for a life without cancer” is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention
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Experience in energy storage Experience with programming languages (ideally Python) Fluent in written and spoken English Very independent and self-motivated way of working but also excellent teamwork skills
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„Research for a life without cancer“ is our mission at the German Cancer Research Center. We investigate how cancer develops, identify cancer risk factors and look for new cancer prevention
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Postdoc (f/m/d): Machine Learning for Materials Modeling / Completed university studies (PhD) in ...
related field # Proficiency in programming languages (Python, C/C++, Julia) # Background in machine learning methods # Experience in developing, training, and tuning machine learning models # Prior exposure