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Postdoctoral Research Associate
Company Information
The Johns Hopkins Bloomberg School of Public Health (JHBSPH) and NYU Grossman School of Medicine (NYUGSoM) are seeking a full-time, highly motivated postdoctoral research associate. This role focuses on advancing data science methods, including artificial intelligence, causal inference, machine learning, and statistics, with applications in population health, health care disparities, outcomes, policy, and services research.
Key Areas of Interest:
• Bayesian learning and inference
• Natural language processing and health informatics
• Fairness in machine learning
• Foundation and large language models
• Causal inference
• Non-/semi-parametric theory and methods (e.g., targeted or double/debiased machine learning)
• Data integration and federated learning
The postdoc will work with Drs. Wenbo Wu (https://wenbowu.me), Iván Díaz (https://idiazst.github.io/website/index.html), Hadi Kharrazi (http://hkharrazi.com), and Christopher Chute (https://publichealth.jhu.edu/faculty/3157/christopher-g-chute). Collaboration opportunities exist with researchers at the Carnegie Mellon University, Harvard University, Johns Hopkins University, New York University, and the University of Michigan.
• U.S. citizen or permanent resident with a doctoral degree in computer science, electrical engineering, (bio)statistics, data science, operations research, or related fields before starting.
• Strong theoretical, communication, and programming skills in R/Python/C++.
• Familiarity with administrative claims, disease registries, and electronic health records is a plus.