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economicssystems/ operations management, econometrics, machine learning, extensive data analytics and qualitative research methods. The student will collaborate closely with advisors to identify, develop, and carry
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APPLICATIONS FOR ADMISSION & UP TO FIVE SCHOLARSHIPS During the first year, you will complete a portfolio of advanced graduate-level courses and seminars covering finance theory, econometric and
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science, information systems, artificial intelligence, operations research, digital twins, high-tech techno-economics, or econometrics or a related field, with a strong focus on semiconductors or chips; Affinity and/or
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approach to research methods that includes, for example, formal theoretical models, quantitative empirical methods such as (lab and field) experiments, econometric applications to register data or survey
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, econometrics, medical statistics, or a related field. You should have demonstrable skills in quantitative research methods and a strong interest in applying these to real-world healthcare challenges. You should
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research and teaching within the various areas of economics. The research, teaching, and dissemination activities at the department are organized within four groups: Econometrics and Data Science; Economic
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Administration Political Science Sociology Economics Statistics and Econometrics Course language Due to the international community, the language of instruction at BAGSS is English. Financial support Yes
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has obtained excellent results in the master phase. Furthermore, the ideal candidate… Has a background (MSc, Research Master) in a quantitative discipline such as (Quantitative) Marketing, Econometrics
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PhD position “Climate Finance: Moral Responsibilities of Financial Institutions for Decarbonization”
, regulation and the public debate. Organisation The PhD position is embedded in the research programme of Economics, Econometrics & Finance of FEB’s Research Institute. The project will be supervised by dr
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Economics, Econometrics, and Finance of FEB’s Research Institute. The project will be supervised by a team with extensive experience using primary and secondary data, namely Sarah Grace See, Mark Treurniet