Important Info
email Marc Lipsitch (lipsitch@stanford.edu)
Opening for Postdoctoral Fellow in Infectious Disease Modeling and Causal Inference: Impact of Nonpharmaceutical Interventions on COVID-19 outcomes
We are recruiting one or two postdoctoral scholars to work on a project at the intersection of causal inference, behavior, and infectious disease modeling. The primary advisor is Prof. Marc Lipsitch in the Division of Infectious Diseases and Geographic Medicine, Stanford School of Medicine, with co-advising by Dr. Mathew Kiang, Assistant Professor of Epidemiology.
The project involves
- developing a rigorous causal framework for assessing the impact of nonpharmaceutical interventions with a well-defined counterfactual
- using data at the state and county levels from the United States to apply the framework and compare its results to those of recently published analyses
- assessing the impact of aggregation, outcome measure, and other analytic choices on results
- developing and implementing a computational model to simulate transmission dynamics of COVID-19 under various nonpharmaceutical interventions with defined effects to assess the identifiability of these effects using various data-analytic approaches.
The fellows will join a team including experienced infectious disease modelers; hence we are primarily searching for
(1) an individual experienced in causal inference, with interest and ideally experience in inference about population-level interventions and/or the challenges of interference;
(2) an individual with interest and ideally experience in modeling behavior change as a function of policy and external events.
This will be an opportunity for the fellow to do consequential work within their own field and to work closely with others with complementary skills and thereby cross-train in fields other than the fellow’s own.
Interested candidates should send a statement of research interests (broadly and in the context of this project), CV, and names and contact info for 3 referees to Marc Lipsitch (lipsitch@stanford.edu)
- PhD in a relevant field is required; relevant fields include but are not limited to epidemiology, statistics, biostatistics, machine learning, data science, or other quantitative fields.
1.statement of research interests (broadly and in the context of this project).
2. CV
3. names and contact info for 3 referees