Applied Machine Learning Postdoctoral Scholar: Exploring Foundation Models in Medicine
About the position: Building on our prior research in developing state-of-the-art representation learning methods for electronic health records (EHR), we are recruiting an applied postdoctoral scholar to develop computational infrastructure for training and evaluating large-scale medical foundation models . The candidate will have access to millions of patient records and include multimodal data for structured codes, clinical notes, medical imaging, and more for research use. We will explore a wide range of  topics, including but not limited to:

• Multimodal Self-supervision
• MLOps for Healthcare Applications
• Evaluating Model Reliability, Usefulness, and Fairness
• Model Adaptation and Transfer Learning
• Robustness
• Language Modeling
• Natural Language Prompting

The position is embedded in a research team whose collaborations span the Human Centered AI Institute (HAI), the Center for AI in Medicine and Imaging (AIMI) and the Clinical Excellence Research Center (CERC) and is co-located with the applied data science team at Stanford Health Care providing an unparalleled opportunity to translate your research advances into better patient care.
About you: You are a hands-on team member with research experience and strong software engineering skills who looks forward to collaborating with medical doctors, statisticians and computer scientists to advance multimodal machine learning and facilitate model implementation in healthcare workflows. You will manage data and compute infrastructure to turbo-charge the research of our entire team. You are a contributor at all levels: designing methods as well as experiments to evaluate them, implementing robust code, and coordinating with our collaborators.
You will find this position to be a good fit if:

• You are passionate about improving health care
• One of your missions in life is to improve research through solid technical infrastructure
• You are excited to work with rich, sometimes messy, patient-level data
• You thrive in dynamic, fast-paced environments

You look forward to responsibilities that include:

• Developing and evaluating methods for multimodal machine learning using the health care data of millions of patients
• Creating and maintaining shared datasets for methods benchmarking
• Iteratively developing code to enable rapid prototyping and evaluation of ML models
• Data wrangling including dataset cleaning and access infrastructure
• Producing scalable, reusable code
• Writing manuscripts and progress reports about your research
• Working in a team of researchers

About us: We are a group of about twenty doctors, engineers, informatics professionals and students focused on enabling better care using existing health data. We develop novel methods to learn from patient-level health data including structured health encounter records, clinical notes, insurance claims, diagnostic imaging, and clinical trial data. Our primary research program is to develop and evaluate safe, ethical, and cost-effective strategies for using predictive models to guide patient care and health system workflows. Our research group is part of the Department of Medicine at Stanford.