Postdoctoral Fellow: Causal ML for Individualized Therapy

American Medical Informatics Association, Inc; Madison (WI); Hybrid; USD 72,000 - 80,000; Full time

Job summary

University of Wisconsin–Madison is seeking a Postdoctoral Research Associate to lead projects at the intersection of causal inference, machine learning, and clinical informatics. The role involves benchmarking causal ML methods on RCT and observational EHR data, and developing models with real-time healthcare impact.

The position offers mentorship, opportunities for first-author manuscripts, travel to conferences, and collaboration across a multidisciplinary lab.

Qualifications

• PhD in computer science, data science, statistics, biostatistics, epidemiology, electrical/computer engineering, biomedical informatics, or 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 (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.

Responsibilities

• Lead manuscripts and pursue first-author opportunities; engage in multicenter collaborations under mentorship.
• Benchmark various causal ML methods for estimating ITE on RCT datasets.
• Assess how observational EHR data can improve ITE prediction.
• Support grant writing and career development activities.
• Mentor UW graduate students and staff as needed.

Skills

Causal inference methods

Machine learning

Python

R

Git version control

Communication skills

Independent and team player

Education

PhD in computer science / data science / statistics / biostatistics / epidemiology / ECE / biomedical informatics

Tools

Python

R

Git

Job description

University of Wisconsin–Madison is seeking a Postdoctoral Research Associate to lead projects at the intersection of causal inference, machine learning, and clinical informatics. The role involves benchmarking causal ML methods on RCT and observational EHR data, and developing models with real-time healthcare impact.

The position offers mentorship, opportunities for first-author manuscripts, travel to conferences, and collaboration across a multidisciplinary lab.