Postdoctoral Research Position in Causal Inference

The Protocol Labs Network is an ecosystem of teams pushing the boundaries of decentralized technologies, research, and open-source innovation.

We invite applications for a full-time Postdoctoral Research Fellow to join the causal inference team supervised by Professor Francesca Dominici. The position will focus on developing and applying novel causal inference methods for large-scale observational studies, with a particular emphasis on environmental exposures and public health. Core data resources include nationwide claims linked with rich contextual information such as census data, weather records, and high-resolution air pollution and related environmental exposures data.

• Causal inference for spatiotemporal data
• Methods for heterogeneous treatment effects estimation
• Methods for multiple exposures, multiple outcomes
• ML and AI methods for causal inference
• Methods for transportability and generalizability of causal effects across space, time, and populations

• Design, develop and implement novel causal inference methods in the areas listed in the position description.
• Work with large, high‑dimensional datasets.
• Lead and contribute to manuscripts for high‑impact journals (e.g., top Statistics journals and Nature‑like journals).
• Present findings in internal meetings and at national/international conferences.
• Collaborate with an interdisciplinary team of biostatisticians, data scientists, computer scientists, and climate scientists.
• Contribute to open-source code and reproducible pipelines.

• PhD (completed or near completion) in Statistics, Biostatistics, Data Science, Computer Science or a closely related field.
• Demonstrated expertise in causal inference, with interest in methods development.
• Experience with statistical and ML methods, including at least one of the following:
  Bayesian methods, deep learning, spatiotemporal modeling, high‑dimensional statistics.
• Proficiency in statistical programming (R and/or Python) and good practices for reproducible research.
• Experience working with large datasets and cloud computing environments.
• Excellent written and oral communication skills, with a track record of peer‑reviewed publications commensurate with career stage.
• Ability to work in a collaborative, interdisciplinary environment.

• Prior experience with one or more of:
  • Health claims data, EHRs, or other large‑scale health/administrative datasets.
  • Environmental, climate, or air pollution exposure data.
• Familiarity with LLMs.

Compensation: $75,000

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