Come to beautiful Burlington, Vermont, and work in a highly collaborative, fun, and dynamic research environment. The fellow will be associated with the Vermont Complex Systems Center and the TGIR Center. The Vermont Complex Systems Center is a post-disciplinary team of faculty, researchers, and students working on real-world, data-rich, and meaningful complex systems problems of all kinds. The TGIR is a Center of Biomedical Research Excellence (COBRE), which brings researchers and clinicians hailing from disparate disciplines together to foster translational science on infectious diseases.

The initial research project that will be conducted by this fellowship include study in any of the following areas:

• Applied projects in network epidemiology, for example on how SARS-CoV-2 has spread in Vermont, a state whose public health response has been exemplary in the United States; and on the impact of patient transfers on the spread of antibiotic resistant pathogens such as C. Difficile.
• Theoretical projects revisiting the foundations of network epidemiology; and the development of statistical estimation methods for models of pathogen spread on networks.
  The fellow will also have considerable freedom to tackle any related topics of interest.

Faculty Mentors
Jean-Gabriel Young, Assistant Professor of Mathematics & Statistics
Laurent Hébert-Dufresne, Associate Professor of Computer Science

Deadline
We will review applications on a rolling basis beginning October 1st, 2021 until November 15th or the position is filled.

Benefits
This 2-year fellowship comes with a competitive salary, discretionary funds, travel funds, and a generous benefits package. Renewal for a third year will be possible. The expected start date is flexible and could be at any time from January 2022 to September 2022.

Eligibility Requirements
A Ph.D. or Master’s Degree (or expected Ph.D./Master’s) in a relevant field (e.g., Epidemiology, Public Health, Networks Science, Computational Biology, Statistics, Mathematics, Computer Science, Data Science, Complex Systems, etc.).
Exemplary knowledge of data analysis, network science or statistical modeling, and the ability to use and learn new statistical techniques and computational tools.
A good familiarity with infectious disease epidemiology.
Ability to work independently and lead a research project from the ground-up.