The research associate/postdoc will work as part of an interdisciplinary team on a USDA–APHIS-funded project, that aims to (1) evaluate the limitations and potential inaccuracies of existing occupancy models that estimate CWD population prevalence and individual infection stage using multiple antemortem tissue samples and lab tests and (2) develop new approaches via sampling design, subsequent laboratory detection methods, and data analysis to reduce or eliminate inaccuracies, while accounting for expected tissue- and test-specific differences in sensitivity and specificity that likely vary among cervid species. We seek a research associate/postdoc with good quantitative skills, a background in disease ecology (preferably CWD or related diseases), and an ability to communicate and collaborate well with a diversity of partners. The research associate/postdoc will review information on existing methods to estimate CWD disease state and infection stage and use simulation to determine potential inaccuracies of these methods with likely dependencies among tissues and imperfect sensitivity and specificity of various lab tests. The research associate/postdoc will develop new approaches via sampling design, subsequent laboratory detection methods, and data analysis to reduce or eliminate inaccuracies, while accounting for expected tissue- and test-specific differences in sensitivity and specificity that likely vary among cervid species. Using this information, they will evaluate sampling design trade-offs and determine optimal sampling and testing strategies to meet common objectives for CWD studies of wild cervid populations, and potentially captive populations. The research associate/postdoc will have opportunities to work locally with collaborators at USDA National Wildlife Research Center (NWRC) and collaborating state agencies (e.g., Nebraska Game and Parks). The research associate/postdoc will work closely with this team of quantitative ecologists and disease ecologists to develop Bayesian approaches to estimate conditional probabilities of disease state and infection status using multiple tissue types and laboratory assays with false positive and false-negative errors. Findings from this work will inform future study design, data collection, and use of laboratory test results for various scientific and management objectives.
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Job Type
Full-time
Career Level
Mid Level
Number of Employees
5,001-10,000 employees