Postdoctoral Researcher (m/f/x) in Causal Inference & Health
Hybrid · New York City, New York, United States or Cambridge, Massachusetts, United States
Job Summary
Postdoctoral Researcher (m/f/x) in Causal Inference & Health to conduct interdisciplinary empirical research on the deployment of clinical AI and health technologies. The role involves designing and evaluating technology adoption studies, analyzing real-world data with causal inference methods, collaborating across economics, clinical research, and computer science teams in Potsdam, New York, and Cambridge, MA, and coordinating stays with partners at Mount Sinai (NYC) and MIT (Cambridge). Responsibilities include supporting data science and health economics/econometrics aspects, coordinating research activities, supervising master theses, and contributing to teaching. Candidates should have a doctoral degree in a relevant field, strong publication record, proficiency in R/Python, and experience with causal inference methods; willingness to conduct extended research stays in NYC and Cambridge and participate in annual partnership workshops.
Required Qualifications
- Doctoral degree in economics, statistics, epidemiology, digital health or a related field
- strong record of first-authored empirical work (publications or working papers)
- programming experience in R and/or Python
- enthusiasm for understanding AI in healthcare and digital health
- hands-on experience with causal inference methods (e.g., matching models, difference-in-differences, IV, RDD, or RCTs) and large administrative or EHR/claims datasets
- independent working style (analytical and operational)
- excellent research and communication skills in English; some German knowledge helpful but not required
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