Postdoctoral Research Position in Causal Inference
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At a glance
Requirements
Credentials this posting asks for.
Job overview
Harvard University is hiring a Postdoctoral Research Position in Causal Inference. Harvard University is seeking a Postdoctoral Research Fellow to join the causal inference team. This full-time position focuses on developing and applying novel causal inference methods for large-scale observational studies, with an emphasis on environmental exposures and public health. The role involves working with nationwide claims data linked with contextual information like census data, weather records, and air pollution data.
Key focus areas include Design, develop and implement novel causal inference methods., Work with large, high-dimensional datasets., and Lead and contribute to manuscripts for high-impact journals..
Successful candidates bring PhD In Statistics/Biostatistics/Data Science/Computer Science, Experience With Statistical And ML Methods, and Experience Working With Large Datasets. Important skills include Causal Inference, Statistical Methods, ML Methods, Bayesian Methods, Deep Learning, and Spatiotemporal Modeling. Preferred (not required): Environmental Data and Climate Data.
Skills & qualifications
Skills
Qualifications
Full job description
Details
Title Postdoctoral Research Position in Causal Inference
School Harvard T.H. Chan School of Public Health
Department/Area Biostatistics
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 the causal effects in complex real-world settings. Among others, methodological areas will span:
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Causal inference for spatiotemporal data,
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Methods for heterogeneous treatment effects estimation,
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Methods for multiple exposures, multiple outcomes,
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ML and AI methods for causal inference,
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Bayesian causal inference,
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methods for transportability and generalizability of causal effects across space, time, and populations.
Duties and Responsibilities
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Design, develop and implement novel causal inference methods in the areas listed in the position description.
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Work with large, high-dimensional datasets.
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Lead and contribute to manuscripts for high-impact journals (e.g., top Statistics journals and Nature-like journals).
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Present findings in internal meetings and at national/international conferences.
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Collaborate with an interdisciplinary team (bio)statisticians, data scientists, computer scientists, and climate scientists.
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Contribute to open-source code and reproducible pipelines.
Basic Qualifications
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PhD (completed or near completion) in Statistics, Biostatistics, Data Science, Computer Science or a closely related field.
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Demonstrated expertise in causal inference, with interest in methods development.
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Experience with statistical and ML methods, including at least one of the following: Bayesian methods, deep learning, spatiotemporal modeling, high-dimensional statistics.
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Proficiency in statistical programming (R and/or Python) and good practices for reproducible research.
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Experience working with large datasets and cloud computing environments.
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Excellent written and oral communication skills, with a track record of peer-reviewed publications commensurate with career stage.
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Ability to work in a collaborative, interdisciplinary environment.
Additional Qualifications
Prior experience with one or more of:
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Health claims data, EHRs, or other large-scale health/administrative datasets.
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Environmental, climate, or air pollution exposure data.
Familiarity with LLMs.
Special Instructions
Please submit the following materials:
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Cover letter describing your research interests, relevant experience, and fit for this position.
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Curriculum vitae including a list of publications.
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One to three representative publications or preprints.
Names and contact information for 2–3 references.
Contact Information
Catherine Adcock
Contact Email [email protected]
Salary Range
$75,000
Minimum Number of References Required 2
Maximum Number of References Allowed 3
Keywords
Causal inference; spatiotemporal modeling; generalizability; transportability; environmental health
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 (https://pa-hrsuite-production.s3.amazonaws.com/606/docs/1678254.pdf) 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 (https://pa-hrsuite-production.s3.amazonaws.com/606/docs/1674140.pdf) . 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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