Description
Matthew Churpek, MD, PhD, Majid Afshar, MD, MS, and Anoop Mayampurath, PhD, of the University of Wisconsin Critical Care Medicine (ICU) Data Science Lab, are seeking a Postdoctoral Research Associate to lead projects at the intersection of causal inference, machine learning (ML) methods, and clinical informatics. Join a unique lab of physician-scientists, clinical informatics experts, ML experts, computational linguistics experts, biostatisticians, engineers, and computer scientists to contribute to cutting-edge research developing models that are closely linked to healthcare practice, with opportunities to implement your work in real time and impact patient outcomes.
Website: https://icudatascience.medicine.wisc.edu/#/
Job Duties:
The Postdoctoral Research Associate will work under the supervision of Dr. Matthew Churpek on a project focused on using causal machine learning methods for estimating individualized treatment effects (ITE) to identify the degree to which a patient benefits or is harmed by a therapy. The successful candidate will help benchmark numerous causal ML methods for estimating ITE in existing randomized clinical trial (RCT) datasets, compare performance across trial characteristics, and determine how observational electronic health record (EHR) data can improve ITE prediction. The Research Associate will lead manuscripts, gain first-author opportunities, receive travel support to present at both clinical and AI conferences (e.g., ATS, AMIA), and participate in multicenter collaborations under close mentorship. They will also gain experience and expertise in grant writing best practices. They may also assist with supervising the work of UW graduate students and staff. Mentors will work with the postdoc on an individual development plan, support career development award applications (e.g., NIH and/or UW opportunities), and provide experiences to help secure future employment opportunities in their field, like other postdocs from our lab.
Begin Date: January 11, 2027, or thereafter. The position is typically 2–3 years, renewable annually
Location: Madison, WI. Hybrid work arrangements available.
Percent Time: 100%
Salary: Approximately $76,000, commensurate with experience.
HOW TO APPLY: Interested candidates should email the following application materials to Madeline Oguss, mkoguss@medicine.wisc.edu:
- Cover letter/summary statement of personal objective and research interests.
- Curriculum Vitae
Note: Reference contact information will be requested of finalists
Institutional Statement on Diversity:
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background - people who as students, faculty, and staff serve Wisconsin and the world.
The University of Wisconsin-Madison is an Equal Opportunity Employer:
Qualified applicants will receive consideration for employment without regard to, including but not limited to, race, color, religion, sex, sexual orientation, national origin, age, pregnancy, disability, or status as a protected veteran and other bases as defined by federal regulations and UW System policies. We promote excellence by acknowledging skills and expertise from all backgrounds and encourage all qualified individuals to apply. For more information regarding applicant and employee rights and to view federal and state required postings, visit the Human Resources Workplace Poster website.
Requirements
Requirements:
- PhD degree in computer science, data science, statistics, biostatistics, epidemiology, electrical/ computer engineering, biomedical informatics, or a closely related quantitative field.
- Demonstrated experience with causal inference methods (e.g., meta-learners, causal forests, doubly robust or targeted estimators, propensity scores) and machine learning.
- Strong programming experience (e.g., Python and/or R).
- Proficiency with Git for version control.
- Strong written and oral communication skills.
- Ability to work both independently and as a team player.
Preferred:
- Additional experience in analyzing EHR and clinical trial data; evaluating heterogeneous treatment effects; and/or clinical research experience.
