Postdoctoral position - Causal inference in electronic health record data

We are looking for a talented researcher with experience in econometric/quasi-experimental approaches for causal effect estimation (e.g., regression discontinuity and difference-in-differences) to help the Geldsetzer lab expand its work on the link between infections and vaccination, and dementia (see: https://www.nature.com/articles/s41586-025-08800-x; https://jamanetwork.com/journals/jama/fullarticle/2833335; https://www.cell.com/cell/fulltext/S0092-8674(25)01256-5; https://www.thelancet.com/journals/laneur/article/PIIS1474-4422%2825%2900455-7/fulltext). Data sources for our work in this area are large-scale electronic health record data, medical claims data, mortality registries, and epidemiological cohort studies. The researcher will be expected to publish in high-impact general science and clinical journals.

This position is part of a collaboration with the Stanford University School of Medicine. Candidates will have the option to become a visiting postdoc at Stanford University.

We are looking for someone to start as soon as possible but there is no specific deadline for the application – we hire on an ongoing basis. Remote and part-time work options are possible.

Required Qualifications:

• Doctoral degree with quantitative training (ideally in econometrics) or relevant research experience.
• Strong coding skills in R, Stata, or other statistical software package.
• Good communication skills in English.

Required Application Materials:

• CV (a cover letter is not required)