In the Stanford Division of Computational Medicine, you will have the opportunity to work in close collaboration with clinicians, scientists, and healthcare systems with access to deep clinical data warehouses (e.g., electronic medical records), implementation and evaluation opportunities in live healthcare systems, and professional development resources like (grant) writing workshops and clinical shadowing experiences.

 

Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating large-language model applications in healthcare systems, systematically identifying ineffective clinical processes, as well as more conventional outcomes research on the implications of clinical practice against challenging issues in healthcare.

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Specific near-term (funded) projects include:

(1) Developing and evaluating "AI doctor" collaborative systems. Includes establishing medical reasoning benchmarks to define what are adequate standards for safe and efficacious behavior of large language models, accompanied by evidence and expert-grounded but automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user interfaces to power automated electronic consultation services to expand access to healthcare services. Developing, deploying, and rigorously evaluating agentic AI systems for medical tasks traditionally reserved for human experts (e.g., medication dose adjustment).

https://med.stanford.edu/hospitalmedicine/research/arise-network.html

https://biox.stanford.edu/research/seed-grants/interdisciplinary-initiatives-program-seed-grant-enhancing-specialty-care

(2) Measuring, predicting, and implementing appropriate antibiotic use for common infections using electronic phenotyping, supervised machine learning, live Epic/FHIR implementations for silent deployment, and multi-site data coordination.
https://reporter.nih.gov/search/ljiYqBbnJkOn3jp2EpXY6g/project-details/10720073#description
 

(3) Develop, deploy, and evaluate software systems and data analytics to improve inpatient hospital care value efficiency. For example, communication dashboards for systematic execution of hospital discharge checklists, analytics to identify gaps between observed vs. documented clinical practices, and use of large language models against live electronic medical records for real-time document generation and coding optimization.

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The position will allow for the exploration of additional research threads further tailored to the applicant's interests and career goals.

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Required Qualifications:

The strongest applicants will have experience in one or more key interdisciplinary areas (not all are expected, that's the point of learning together):

Computer Science or Informatics:
Proficiency in programming and software development with a habit for robust unit testing. Our group mainly develops software in a Python + SQL environment with use of large language model and electronic medical record FHIR APIs, cloud computing environments, and R for additional statistical analysis. For decision support prototype development and evaluation, web-based user interface design, human-computer interaction testing experience can be valuable. Vibe coding is fine, but needs to be backed by experience and knowledge to design, architect, and debug complex software systems.

Statistics and Mathematics:
Machine learning (supervised and unsupervised) methodology and evaluation including discrimination vs. calibration measures and (hyper)parameter optimization through cross-validation. Observational research methods including interpreting multivariate regression, missing data imputation, propensity score matching, and bootstrap simulations.

Biomedical / Healthcare Science:
Understanding of clinical decision-making processes, healthcare quality metrics, financial incentives, and decision support interfaces and pitfalls.

A Ph.D. in a quantitative field with a strong programming and statistics background

Track record of completed research projects

Well-written, peer-reviewed papers are expected.

Specific responsibilities and research projects will be tuned to the career goals, technical strengths, and interests of the applicant.

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Required Application Materials:

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CV

Example research paper

2-3 references

A brief career goal statement (that reflects alignment with the projects we would likely pursue together)

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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.

 

Important Info:

Faculty Sponsor (Last, First Name):

Chen, Jonathan

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Stanford Departments and Centers:

Computational Medicine

Med: General Internal Medicine

Neurology & Neurological Science

Psychology

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Postdoc Appointment Term:

1 year minimum with the option to extend. $75,000+ per year + post-doc benefits

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Appointment Start Date:

Immediate

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How to Submit Application Materials:

Email application materials to jonc101@stanford.edu