Please apply by sending a CV to Pascal Geldsetzer at firstname.lastname@example.org. A cover letter is not required. There is no specific deadline for the application – we hire on an ongoing basis.
Causal inference in electronic health record data
This postdoctoral fellowship is a collaboration between Stanford University and the Heidelberg Institute of Global Health at Heidelberg University, Germany’s oldest university and a leading hub for health-related research in Europe. We are looking for a quantitative researcher with an interest in working with novel causal inference techniques in electronic health record and claims data. The researcher will work under the mentorship of Dr. Pascal Geldsetzer, as well as other researchers at Stanford and Heidelberg University.
The researcher will analyze large-scale electronic health record data from the United Kingdom, Germany, Denmark, or the United States. The broad aim of these analyses is to establish the causal effect of commonly prescribed medications and/or clinical interventions on health outcomes, with a key focus being on how these effects differ across a wide variety of patient characteristics. The researcher will be expected to publish in high-impact peer-reviewed journals.
- Doctoral degree with quantitative training or research experience.
- Training and experience in quasi-experimental techniques is a plus.
- Strong coding skills in R, Stata, or other statistical software package.
- Good communication skills in English (knowledge of German is not necessary).