Statistics Forms

Company NameYale University

Position TitlePostdoctoral Associate in Statistics

Company Information

The Department of Biostatistics at Yale School of Public Health (YSPH) is home to Biostatistics faculty who are leaders in developing quantitative methodologies and tools for rigorous scientific research to solve the world's most challenging problems in biology, medicine, and public health.

Duties and Responsibilities

We are seeking outstanding postdoctoral candidates with a strong interdisciplinary background across statistics, biostatistics, and computer science. The candidate will engage in cutting-edge methodological research, tackling statistical challenges that arise from randomized clinical trials and observational studies. The successful candidate will work with Dr. Laura Forastiere and Dr. Fan Li to develop new causal inference methods in the areas of interference, social networks, causal mediation, principal stratification, complex outcomes, and causal machine learning for understanding treatment effect heterogeneity. The candidate will also have a chance to collaborate with multidisciplinary researchers at Yale on applying novel methods to real-world data, and on developing grant writing skills. Potential projects include: (A) causal inference in randomized experiments for treatment and spillover effects on networks when networks are measured with error; (B) causal inference in complex observational studies, with a time, space, and/or network component; (C) causal mediation analysis to disentangle spillover effects from other’s mediators and non-compliance; (D) design and analysis of experiments involving network data; (E) optimal targeting strategies under heterogeneous interference. The ideal project develops a new quantitative approach and applies it to an important problem. Areas of special interest include social and economic networks, static and dynamic network formation models, diffusion models, causal inference under interference, non-compliance, optimal policy, and optimal design of experiments.

Position Qualifications

Qualifications required are a PhD in statistics or biostatistics or relevant areas, a strong background in statistical methodology, good writing and communication skills. The ideal candidates will have deep knowledge of statistics and machine learning, and a strong track record of research in quantitative methods, with interests toward applications in computational social science or public health, evidenced by high quality publications; be able to communicate and collaborate with student/postdocs and external PIs; and be able to carry out research and develop ideas independently. Programming skills (e.g., R, Python) are also required. Candidates whose doctoral dissertation focused on causal inference, semiparametric methods, study design, and network science are encouraged to apply.

Salary RangeSalary will be based on the NIH postdoctoral research salary scale, and support for travel to conferences is also available.

Benefits

Information on benefits for postdoctoral positions can be found at https://postdocs.yale.edu/postdocs/benefits

Position or Company Websiteyale.edu

Application Instructions

To apply, please submit an academic CV, cover letter, 1-2 representative publications, and contact information for 3 references to: Dr. Laura Forastiere at laura.forastiere@yale.edu and Dr. Fan Li at fan.f.li@yale.edu. In your cover letter, please summarize your relevant research experience and indicate your research interest and the date you will be available to start. Applicants must have completed all PhD requirements prior to starting work. Scientific questions regarding this position should be directed via email.

Review of applications will begin immediately and continue until positions are filled.

Application Deadline01/01/2026

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