The Department of Anesthesia and Perioperative Care at UCSF and the EpochAI lab are seeking a highly motivated Postdoctoral Scholar with strong expertise in machine learning (ML) and causal inference to join an NIH R01-funded project focused on improving early treatment decisions for patients with community-onset lung sepsis (COLS).
Requirements
- PhD (or equivalent) in computer science, statistics, epidemiology, biomedical informatics, or related field
- Demonstrated experience in machine learning, causal inference, and/or clinical prediction modeling
- Proficiency in Python and/or R, and experience working with large-scale clinical data
- Familiarity with counterfactual reasoning and model evaluation metrics (e.g., calibration, discrimination, bias)
- Experience working with electronic health records (EHR) or claims data
- Familiarity with implementation science or human-centered design
- Knowledge of Bayesian models, bootstrap estimation, or semi-supervised learning
Responsibilities
- Develop and validate machine learning models to estimate counterfactual outcomes and individual treatment effects (CATE/ITE) using EHR data
- Apply advanced modeling techniques including: Highly Adaptive Lasso, Generative Adversarial Networks (GANs), and other interpretable ML methods
- Causal inference frameworks (e.g., T-Learner, DR-Learner, G-computation)
- Collaborate with clinical teams to translate model output into actionable CDSS recommendations
- Contribute to the design and testing of an EHR-embedded user interface for the CDSS
- Lead and contribute to scientific publications, presentations, and progress reports
- Participate in regular project meetings and collaborative data science discussions
Other
- Strong communication and scientific writing skills
- Ability to work independently and collaboratively in a fast-paced, interdisciplinary environment
- CV
- Cover letter describing your interest in the position and relevant experience
- Contact information for 2–3 references
