Open Postdoctoral position, faculty mentor Zachary Butzin-Dozier

Posted on Mon, 03/30/2026 - 14:35

Important Info

Faculty Sponsor First name: 
Zachary
Faculty Sponsor Last Name: 
Butzin-Dozier
Stanford Departments and Centers: 
Pediatrics
Postdoc Appointment Term: 
1 year (renewable)
Appointment Start Date: 
June 1, 2026 (flexible start date)
Group or Departmental Website: 
https://butzindozier.com
How to Submit Application Materials: 

Please email application materials to zdozier@stanford.edu. 

Does this position pay above the required minimum?: 
No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral scholars appointed through the Office of Postdoctoral Affairs. The FY25 minimum is $76,383.

Appointing a postdoctoral scholar with expertise in causal inference, machine learning, and analyses of electronic health record (EHR) data. The postdoc will work with Assistant Professor Zachary Butzin-Dozier (Department of Pediatrics, Division of Clinical Informatics, and Department of Medicine, Division of Computational Biology) on research using large-scale EHR data sources, including Epic Cosmos and PEDSnet.

Ideal candidates will have experience in one or more of the following areas: EHR-based research, machine learning or artificial intelligence (e.g., large language models, EHR foundation models), causal inference (e.g., target trial emulation), and child health research. The research program focuses on applying modern causal inference and machine learning methods to observational EHR data.

This role offers substantial opportunities for first-author publications, close mentorship, and increasing research independence. As a new faculty member building an expanding research program, Dr. Butzin-Dozier provides a dynamic environment with growing resources, including research staff support and access to multiple large-scale data platforms.

More information about recent research, including work on preventive interventions for Long COVID, is available at https://butzindozier.com

Required Qualifications: 
  • PhD (or equivalent) in Biostatistics, Epidemiology, or a closely-related quantitative field (by start date)
  • Strong training in causal inference methods
  • Experience working with large-scale observational health data, particularly electronic health records (EHR)
  • Proficiency in statistical programming (e.g., R or Python) and reproducible research workflows

  • Demonstrated ability to lead analyses and contribute to peer-reviewed publications

    Preferred Qualifications:

    • Experience applying machine learning and artificial intelligence methods in healthcare data settings
    • Familiarity with EHR data models and networks (e.g., Epic Cosmos, PEDSnet, OMOP, PCORnet, N3C)
    • Experience integrating causal inference with machine learning approaches (e.g., targeted machine learning)
    • Prior work in child health, clinical informatics, or health services research
    • Experience working in interdisciplinary teams involving clinicians, informaticians, and data scientists
Required Application Materials: 
  • A cover letter
  • CV
  • Contact information for 2 references
  • 1-3 recent first-author publications or pre-prints (all materials combined as a single PDF)

 

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.