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Open Postdoctoral position, faculty mentor Pascal Geldsetzer

Important Info

Faculty Sponsor (Last, First Name): 
Geldsetzer, Pascal
Stanford Departments and Centers: 
Med: Primary Care and Population Health
Epidemiology and Population Health
Biomedical Informatics
Biomedical Data Sciences
Postdoc Appointment Term: 
1-5 years (negotiable)
Appointment Start Date: 
How to Submit Application Materials: 

Please apply by sending the required application materials to Pascal Geldsetzer at

Does this position pay above the required minimum?: 
Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity.
Pay Range: 
$68,238 - $90,000

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.
There is no specific deadline for the application – we hire on an ongoing basis.

Required Qualifications: 
  • 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).
Required Application Materials: 
  • CV (a cover letter is not required)


Stanford is an equal opportunity employer and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other characteristic protected by law.