The post-doctoral associate will be supervised by Krishna Pacifici (Forestry and Environmental Resources), Brian Reich (Statistics), and Rob Dunn (Applied Ecology) and housed in the Departments of Forestry and Environmental Resources and Statistics at North Carolina State University. There will be the opportunity to interact with other scientists on the project and travel to Colorado and Hawaii.
North Carolina State University is seeking a post-doctoral position focused on statistical ecology primarily working on a large collaborative NSF EEID project “Unearthing the environmental, host, and nontuberculous mycobacterial factors that interact to cause lung disease in the Hawaiian Islands”. The candidate will also have the opportunity to work on a wide variety of projects in addition to the NSF study including species distribution modeling, data integration, movement ecology, spatiotemporal modeling and environmental statistics. The candidate will be required to spend 50% of their time on the NSF EEID project and the rest of their time can be spent exploring other projects with collaborators at NCSU (Forestry and Environmental Resources, Statistics, Applied Ecology), Duke, UNC, the NC Museum of Natural Sciences or elsewhere.
NSF EEID Project Objectives: The post-doctoral associate will work closely with a range of scientists (medical doctors, ecologists, statisticians, and epidemiologists) to build spatiotemporal models that predict the development of Nontuberculosis Mycobacteria (NTM) lung disease based on disease characteristics, environmental and host factors. The goal will be to exploit an unprecedented number of environmental and host samples to test a suite of hypotheses concerning how NTM spreads through space and time and how the many contributing factors (disease characteristics, environmental conditions, and unique host features) influence this spread. There will be a great opportunity for the associate to explore multiple avenues of research (spatiotemporal modeling, data integration) and to develop their own specific research interests within the larger context of the project.
PhD in Quantitative Ecology, Statistics, Disease Ecology or related field with strong emphasis in spatiotemporal statistical modeling. Competitive applicants will be familiar with a wide range of approaches to model complex spatiotemporal data and the ability to develop and apply models to very large data. Applicants should be very familiar with at least one of the following: R, C++, or python and have the ability to develop and fit hierarchical Bayesian models outside of the BUGS/JAGS environment (due to the very large amount of data expected).