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.

Motivated by relevant public health and policy questions, the goal is to develop methodologies for the identification, estimation, transportability, and generalization of the causal effects in complex real-world settings. Among others, methodological areas will span:
● Causal inference for spatiotemporal data,

● Methods for heterogeneous treatment effects estimation,

● Methods for multiple exposures, multiple outcomes,

● ML and AI methods for causal inference,

● Bayesian causal inference,

● methods for transportability and generalizability of causal effects across space, time, and populations.

Duties and Responsibilities 

● 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 (bio)statisticians, 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. 

Catherine Adcock

$75,000