Open Postdoctoral position, faculty mentor Tina Hernandez-Boussard

The postdoctoral researcher will conduct methodological research at the intersection of AI reasoning, biomedical informatics, and clinical medicine. Our work focuses on understanding how AI systems reason over complex longitudinal patient information, how reasoning can be combined with data-driven models of patient trajectories, and how we can rigorously determine when AI-generated clinical decisions are reliable.

A central focus of the lab is developing rigorous methods to determine when clinical AI works, for whom, under what conditions, and with what information. We are particularly interested in approaches that move beyond conventional model performance to evaluate reliability, transportability, uncertainty, information sufficiency, and clinical usefulness.

The postdoc will work with large-scale clinical data resources at Stanford and through national data networks and will have access to the computational and informatics infrastructure required for clinical AI research. The position offers opportunities to work at the intersection of methodological development and real-world clinical evaluation.

The researcher will join a multidisciplinary team of faculty, postdoctoral researchers, engineers, clinicians, statisticians, informaticians, and trainees. The postdoc will be expected to take intellectual ownership of a research program, lead scientific manuscripts, present findings at national and international meetings, and collaborate with clinical and technical partners at Stanford and beyond.

We are particularly interested in candidates with backgrounds in biomedical informatics, computer science, machine learning, statistics, data science, computational biology, or related quantitative disciplines. Experience with longitudinal health data, machine learning, natural language processing, large language models, causal or counterfactual methods, or clinical informatics is desirable.