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to characterize the molecular properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and
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properties of catalysts together with statistical methods to derive predictive models for selective catalysis. In a data-driven approach, an initial set of reactions is analyzed and used to establish such a
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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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Associated Applied Statistics and Empirical Methods (ASEM) Programme Faculty of Chemistry Chemistry Thematic programme Catalysis for Sustainable Synthesis (CaSuS) Programmes Faculty of Geoscience and Geography
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German Institute for Economic Research (DIW Berlin) • | Berlin, Berlin | Germany | about 19 hours ago
engineering, statistics, or quantitative social sciences) may also fulfil requirements for the programme if applicants have sufficient background knowledge in economics. Applicants whose native language is not
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Studies and have performed exceptionally well in their studies. Applicants from related fields (such as Industrial Engineering, Statistics, or quantitative social sciences) may also fulfil the admission
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researchers. Faculties Human Sciences and Education Educational Science Psychology Social Sciences, Economics, and Business Administration Political Science Sociology Economics Statistics and Econometrics
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cybersecurity research. Who you are: You have BS in machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years
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mental health issues, using statistical techniques including network analysis. Naturally, we are seeking a student with a keen interest in the topic of loneliness and/or mental health. In addition
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, engineering physics, biomedicine, or similar Documented skills in data-driven analysis (machine learning using python with TensorFlow, PyTorch, or similar) and computational statistics Specific knowledge of big