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Infrastructures Didactics of Informatics Digital Humanities Distributed Systems High-Performance Storage Machine Learning Medical Informatics Neural Data Science Practical Informatics Scientific Information
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environmental conditions and the analysis and modelling of the consequences of interventions in biogeochemical cycles. The social science methodology is included in the context of human ecology, political ecology
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computer skills. We also expect successful candidates to be active team players in the RTG. Experience with data collection in an international context will be a plus. Language requirements Excellent command
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expert researchers teach in more than 40 academic disciplines. More than 40 full-time undergraduate and graduate programmes are taught entirely in English. Two international fast-track MSc/PhD programmes
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committees advising the doctoral student and monitoring the progress of the doctoral project Scientific methods courses, seminars and summer schools in all science areas covered by the graduate school
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professionals Especially for international students aiming for a career entry in Germany, the Career Service provides workshops, online learning modules, and a qualification programme as well as digital career
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to 1,500 EUR per month. Academic admission requirements Master's degree or equivalent in the life sciences, mathematics, computer sciences, statistics, biophysics, or a related field Language requirements
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formations are of growing economic and societal importance. By participating in a number of externally funded projects, doctoral candidates learn to perform independent scientific work. Through active
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research institutions. Main features: Research-based teaching and learning – featuring intensive training and cutting-edge research projects in pure and applied mathematics, emphasising independent research
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Career Service offers individual support whether you want to work in Germany or abroad. Especially for international students aiming for a career entry in Germany, there are workshops, online learning