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Field
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MPhil) in Economics, Econometrics, Finance, Mathematics, Business Administration, or a related field. Graduation from a two-year Master’s programme (120 ECTS) is an advantage. Research skills – Knowledge
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skills. Strong academic background – You hold (or will obtain by winter 2025/2026) a Master’s degree (MSc or MPhil) in Economics, Econometrics, Finance, Mathematics, Business Administration, or a related
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(IMI, IMT, IRS, Census) with descriptive methods and causal econometric techniques. It will use various approaches to identify vacant dwellings, including machine learning algorithms to visually detect
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and oral communication skills in English. Ability to work independently and as part of a team. Prior experience with large datasets, econometrics, or geospatial analysis is a strong asset. We offer We
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(Department of Data Science and AI in the Faculty of Information Technology) and Associate Professor Susan Wei (Department of Econometrics and Business Statistics in the Faculty of Business and Economics
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knowledge of quantitative methods, particularly in statistics and econometrics; experience in machine learning is a plus Background in business/management/behavioral science Experience with programming
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, initiative, and the capacity to work effectively in a team are essential. Prior experience with econometric software (e.g., Stata, R) and field surveys in developing countries will be considered an asset
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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 a strong team
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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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data analysis and operations research. You hold, or will shortly acquire, a Master’s degree in Civil Engineering, Industrial Engineering, Mechanical Engineering, Econometrics, Computer Science, or a