Postdoctoral Research Position in Causal Inference and Data Science for Environmental Health Policy
Harvard T.H. Chan School of Public Health
The Department of Biostatistics at the Harvard T.H Chan School of Public Health invite applications for Postdoctoral fellow positions to develop causal inference methods for the analysis of large-scale environmental health data with anticipated start dates in Summer/Fall 2019 (flexible). The position is in the lab of Dr. Francesca Dominici, Clarence James Gamble Professor of Biostatistics, Population and Data Science and co-Director Harvard Data Science Initiative. Applicants should have an interest in developing statistical methodological innovations grounded in environmental health, climate change and policy applications. The Department of Biostatistics is a multidisciplinary research department at the forefront of biostatistics and data science.
The specific position involves methodological work in the areas of:
- Bayesian causal inference,
- Machine learning, and
- Causal inference using modern optimization approaches.
The Post-doctoral fellow will contribute to the effort of:
- Developing innovative methods for causal inference in the context of large observational data.
- Analyzing environmental health effects in big data.
- Developing and disseminating software for reproducible research.
- Creating innovative web-based data visualizations.
- Writing scientific articles and research proposals.
Doctoral degree in Statistics, Biostatistics, Computer Science, or related field. Familiarity with causal inference methods using observational data; strong programming skills, especially for conducting simulation studies; and experience analyzing real data 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 developing statistical methods, and driving toward actionable insights and novel solutions.
- PhD in Statistics, Biostatistics, Computer Science, Data Science
- Strong background in statistics and computational methods.
- Interest in developing open-source software, reproducibility.
- Experience in handling very large spatial datasets.
- Familiarity with multiple data science tools and ability to learn new tools as required.
- Demonstrated ability to contribute to research of new statistical approaches, inference algorithms.
- Experience with version control systems, in particular Git and GitHub.
- a cover letter
- a curriculum vitae
- 1 page research statement
- at least three names for references
Administrative questions regarding this position can be sent to Susan Luvisi at firstname.lastname@example.org.
Scientific questions regarding this position can be sent to Francesca Dominici at email@example.com.
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.