The W.A. Franke College of Forestry & Conservation at the University of Montana seeks applicants for a 12-month Postdoctoral Research Associate with potential for a 12-month extension contingent on funding availability to join their team on campus. The postdoctoral researcher will work with Dr. Paul Lukacs and collaborators at the USGS National Wildlife Health Center, Iowa Department of Natural Resources, and other project partners to develop and expand models for estimating animal abundance from remote camera data. The post-doc will work to develop sampling designs and analysis methods that improve estimation for deer in areas at risk of chronic wasting disease. The post-doc will also implement the analysis methods with data collected by partner agencies. Expected products include scientific publications, reports, and annotated analysis code.
We seek a highly motivated post-doctoral scholar to examine design of remotely triggered trail camera surveys and the use of camera data for density estimation. Chronic wasting disease (CWD) poses a serious health risk to cervid populations throughout North America. Understanding patterns of cervid density across space and time remains a challenging question but is essential for determining and assessing the effects of CWD management efforts. Recent advances in the use and analysis of trail camera data offers potential for improved inference to cervid density. Yet, questions remain about camera sampling design and analysis techniques. Therefore, the post-doctoral researcher will examine analysis and design considerations for the use of trail cameras for estimating cervid density.
Minimum Required Experience
Must have a PhD in statistics, applied mathematics, ecology/biology, or closely related field by the start of employment
Must also have demonstrated experience publishing scientific papers in the areas of statistics or quantitative ecology
Must have the ability to develop and implement Bayesian statistical model
Proficiency with the R software, experience with custom fitting Bayesian models, knowledge of sampling theory