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master) in Economics or Econometrics, preferably with a specialization in international economics and/or development economics. Familiarity with STATA or similar software packages is required; experience
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management, psychology, social sciences, economics/econometrics or any other (quantitative) social science discipline. We are also open to backgrounds in engineering or computer science. For the project, we
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models. This project has the potential to expand into broader econometric applications and utilize machine learning methods to address empirically relevant questions in disciplines such as empirical
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-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between environmental stressors and population health. This project is part of a larger project ECOTIP
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results in the Master phase. The PhD candidate should have a background (MSc, Research Master) in Economics or Econometrics, preferably with a specialization in international economics and/or development
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different levels of the environmental stress index. In addition, it will employ cross-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between
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phase. The PhD candidate should have a background (MSc, research master) in Economics or Econometrics, preferably with a specialization in international economics and/or development economics. Familiarity
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, research master) in business or management, psychology, social sciences, economics/econometrics or any other (quantitative) social science discipline. We are also open to backgrounds in engineering or
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the Master phase. The PhD candidate should have a background (MSc, Research Master) in business or management, psychology, social sciences, economics/econometrics or any other (quantitative) social science
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addition, it will employ cross-sectional and longitudinal econometrics and machine learning techniques to determine the relationship between environmental stressors and population health. This project is