Postdoctoral Research Position in Causal Inference

Harvard University; Harvard (IL); On-site; USD 67,500 - 82,500; Full time

Job summary

Harvard University is seeking a full-time Postdoctoral Research Fellow to work on developing and applying novel causal inference methods for large-scale observational studies focusing on environmental exposures and public health. The position requires a PhD in a relevant field and involves working with large datasets, contributing to high-impact journal publications, and collaborating with a diverse team. The role offers a salary of $75,000 annually and excellent career development opportunities.

Qualifications

• PhD (completed or near completion) in Statistics, Biostatistics, Data Science, or a closely related field.
• Demonstrated expertise in causal inference and methods development.
• Experience with statistical and ML methods, including Bayesian methods or deep learning.

Responsibilities

• Design, develop and implement novel causal inference methods.
• Work with large, high‑dimensional datasets.
• Lead and contribute to manuscripts for high‑impact journals.

Skills

Causal inference

Statistical programming in R and/or Python

Machine learning methods

Communication skills

Collaboration in interdisciplinary teams

Education

PhD in Statistics, Biostatistics, Data Science, or related field

Tools

Cloud computing environments

Job description

School

School Harvard T.H. Chan School of Public Health

Department/Area

Position Description

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 causal effects in complex real-world settings.

Methodological Areas

• 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

Duties and Responsibilities

• Design, develop and implement novel causal inference methods in the areas listed above.
• 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.

Basic Qualifications

• 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.

Additional Qualifications

• Prior experience with health claims data, EHRs, or other large‑scale health/administrative datasets.
• Prior experience with environmental, climate, or air pollution exposure data.
• Familiarity with LLMs.

Contact Information

Catherine Adcock

[e-mail]

Salary Range

$75,000

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes. Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy. Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.

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