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Frankfurt School of Finance & Management • | Frankfurt am Main, Hessen | Germany | about 12 hours ago
, Finance, and Management. You will develop strong analytical skills, publish in top journals, and join a global academic network. This fully-funded programme includes advanced courses, close faculty support
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. Qualifications The applicants should possess: An excellent or very-good university degree in economics, business studies, agricultural sciences with a focus in economics, or related disciplines Strong analytical
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-analytic methods (e.g., meta-analytic structural equation modeling) We are looking for a motivated researcher who is eager to contribute to a vibrant and growing field with strong societal relevance. Your
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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Areas of study Neuroscience, Physics, Applied Computer Science, Bioinformatics, Computer Science, Engineering Informatics, Medical Informatics, Chemistry, Analytical Chemistry, Inorganic Chemistry
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conditions for you to complete your doctoral degree: Excellent scientific and technical infrastructure for conducting high-quality research Working with globally unique analytical measurement methods
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practical experience in at least one programming language (preferably Python) Ideally, some practical experience in material characterization methods Structured and analytical thinking as well as a systematic
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) Optimization and tuning of catalyst layer performance and durability: morphology, hydrophobicity, porosity Product analysis and analytical chemistry; investigation of side reactions System process analysis and
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The Leibniz-Institut für Analytische Wissenschaften - ISAS - e. V. develops efficient analytical methods for health research. Thus, it contributes to the improvement of the prevention, early
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a focus in economics, or related disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation