We’re seeking talented post-doctoral researchers to join our interdisciplinary research team on an ambitious multi-year mission to develop cutting-edge computational tools for monitoring, diagnosing, and updating ML/AI algorithms. The goal is to ultimately rigorously test and deploy these tools in multiple hospital systems. If this opportunity excites you, please see below! Note that all posted positions will be 100% funded for two years, with potential for continuation. Salary and benefits are competitive.
Postdoctoral Fellow in monitoring clinical impact of AI tools
This project aims to develop novel statistical and computational methods for monitoring changes in the clinical impact of AI-enabled devices, such as AI scribes and clinical risk prediction models. A critical aspect of this project is to think through how the AI’s impact is mediated through the user of the AI device, and how human+AI interactions affect what we can (or cannot) monitor. This project is co-led by Drs. Jean Feng and Fan Xia from UCSF as well as the FDA’s CDRH/OSEL/DIDSR team, and will be supported by the UCSF IMPACC group.
Primary Responsibilities
- Develop tools for monitoring AI-enabled medical devices, with rigorous statistical guarantees
- Extract, process, and analyze multi-modal Electronic Health Record (EHR) data (e.g., clinical notes)
- Design and implement open-source software packages and federated data pipelines
- Collaborate closely with interdisciplinary team
- Prepare and submit research manuscripts to ML/AI conferences, statistics journals, and/or clinical AI journals
Qualifications
The postdoctoral researcher position requires a PhD in statistics, biostatistics, computer science, data science, or a related field. We are looking for someone who:
- Has strong experience in methodological development and independent research, with a strong publication record
- Has a strong computational background and is comfortable processing large-scale, multi-modal datasets
- (Preferably) Has methodological expertise in one or more of: causal inference, sequential monitoring, changepoint detection, ML/AI, natural language processing, federated learning, longidutinal data analysis
Postdoctoral Fellow in developing clinical AI monitoring systems
This project aims to develop a suite of statistical and computational methods that hospitals can use to monitor changes in performance of clinical AI algorithms and, subsequently, diagnose the root cause and update the model. The team is led by Drs. Feng and Suchi Saria. Other team members include faculty across UCSF, JHU, and Northwell with expertise in ML/AI/statistics and medicine, including Fan Xia, Julian Hong, Lucas Zier, Arjun Sondhi, and more!
Primary responsibilities:
- Develop tools for monitoring, diagnosing, and updating ML/AI systems in real-world clinical settings with rigorous statistical guarantees. See example papers from our group here, here, here, here, and here.
- Design and implement robust, scalable software libraries and data pipelines
- Collaborate with deployment teams to ensure developed tools work well in real-world settings
- Prepare and submit research manuscripts and technical reports
Qualifications:
The post-doctoral researcher position requires at least a PhD degree in computer science, (bio)statistics, data science, or related fields. We are looking for someone who:
- has experience in methodological development and can perform independent research, with a strong and relevant publication record
- (preferably) has experience in at least one of these fields: sequential monitoring, multiple hypothesis testing, machine learning, causal inference, post-prediction inference
- is comfortable working with large datasets as well as data from different modalities (tabular, imaging, text)
- is able to work collaboratively with a team
Applying
If you are interested in either position, please submit the following materials to :
- A cover letter
- A CV summarizing your education and work experience so far
- The names and email addresses of three references
- A github repo of yours that you are most proud of
- One representative publication
Screening of applicants will begin immediately and will continue as needed throughout the recruitment period.
Updated 09/17/2025
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.