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Harvard University Postdoctoral Research Position in Biostatistics in Cambridge, Massachusetts


Title Postdoctoral Research Position in Biostatistics

School Harvard T.H. Chan School of Public Health

Department/Area Biostatistics

Position Description

The Department of Biostatistics at the Harvard T.H. Chan School of Public Health invites applications for a Postdoctoral Fellow position for statistical methods development to address questions of climate change and air pollution regulatory policy. Expertise in causal inference and machine learning is desirable but not necessary. The Postdoctoral Fellow will work with a multi-disciplinary team with expertise in statistical methods, epidemiology, climate science, and biomedical science. The position will be under the joint supervision of Dr. Francesca Dominici in the Department of Biostatistics and Dr. Rachel Nethery, and in collaboration with Dr. Danielle Braun. Applicants should have an interest in developing and applying novel and state-of-the-art statistical and data science methods in environmental health.


Doctoral degree in Computer Science, Statistics, Biostatistics, or related field.

Experience in analyzing real data, strong programming skills, and familiarity with statistical methods is preferred.

Excellent communication and writing skills desired.

The ideal candidate is an independent, solution-oriented thinker with a strong background processing very large data sets, applying analytical rigor and statistical methods, and driving toward actionable insights and novel solutions.

Duties and Responsibilities

The Post-doctoral Fellow will contribute to the effort of:

· Analyzing environmental health effects in big data

· Collaborate with our biostatistics and data science group

· Writing scientific articles and research proposals

Basic Qualifications

Basic Qualifications

· PhD in Biostatistics, Statistics or Computer Science.

· Experience in air pollution health studies (desired).

· Experience in handling very large spatial datasets.

· Experience in applied statistics and computational methods.

· Knowledge of R, SAS , and Python.

· Interest in open-source software, reproducibility and data management.

Additional Qualifications

Additional Qualifications

· Familiarity with multiple data science tools and ability to learn new tools as required.

· Experience with version control systems, in particular Git and GitHub.

Special Instructions

Contact Information

Susan Luvisi

Contact Email

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required 2

Maximum Number of References Allowed 4


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