Postdoctoral position - Causal inference in electronic health record data

Stanford University

Medical School

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.medrxiv.org/content/10.1101/2023.05.23.23290253v1). 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.

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. So, candidates wanting to start in Summer/Fall 2025 are welcome to apply as well.

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: