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Causal Inference Postdoctoral Scholar

The Arnhold Institute seeks to reduce health inequities via innovative research and products. We have an opening for a causal inference postdoctoral scholar to combine machine learning and econometrics approaches to health data. The position will focus on the scholar's career development by developing and executing a rigorous research program and developing new collaborations across healthcare. We have access to very rich (and large) health datasets that can be a significant career development asset. While the program will depend on the candidate but we anticipate the position to require 50% research, 25% writing, 15% career development, and 10% travel.

Responsibilities

  • Contribute to a creative research program with a focus on causal inference, machine learning, and health.
  • Lead and co-lead the publication of interdisciplinary studies at the intersection of causal inference and health.
  • Work with domain experts to translate machine learning science into useful products.
  • Travel to present at conferences and events to bring exposure to our work.
  • Be an active community member to help grow the "causality in machine learning" community
  • Lead and co-lead grant proposal preparation to advance the state-of-the-art of data-driven scientific discovery.

Qualifications

  • The successful candidate must be intellectually flexible with a proven track record of interdisciplinary work. We look for candidates who continuously step outside their comfort zone and work well with others outside their domain of expertise.
  • Algorithmic experience with large-scale predictive and inferential models. This is not a theoretical position. 
  • Experience with experimental design and statistical analyses related to the generalizability of statistical models (e.g. reusable holdout).
  • Working experience with causal inference from observational data, demonstrated by publications and/or link to software
  • A Ph.D. in computer science, statistics, or applied mathematics.