Position Summary:

The Department of Fisheries and Wildlife, Michigan State University seeks a research associate to play an important role in a quantitative project examining the application of meta-analysis (MA) in ecology and evolution. MA is a powerful tool that plays a critical role in science and informs policy. We have identified several potential issues in the application of MA which may be exacerbated by the nature of ecological data. We seek to ascertain conditions under which different MA methods work best, including methods developed during the project, given the characteristics of ecological data. This will be done with experiments using simulated data with known properties; characteristics of data (e.g., sample sizes, how variation is allocated among sources) and MA method (e.g., effect size metric, variance estimators) will be systematically varied. By probing performance under known conditions, the results will reveal the conditions in which each method can best be applied (e.g., is efficient, relatively unbiased, and provides valid assessment of uncertainty). This will yield "best practices" for ecologists and make the application of MA in ecology more reliable and robust. The postdoc will work as part of a team that involves James Bence and Scott Peacor (Michigan State Univ.), Craig Osenberg (Univ. of Georgia) and graduate students. The postdoc will be hosted at MSU's Quantitative Fisheries Center (QFC), and have opportunities to interact with QFC staff working on a range of quantitative ecological and natural resource issues. The incumbent may use this as an opportunity to perform MA on a topic of their choice in a way that compliments the ongoing project.



Initial appointment is for 1 year with the possibility of extension for subsequent years based on performance and funding.
Faculty: Minimum Qualifications Support Staff: Minimum Requirements

Doctorate degree. Preference will be given to candidates with strong programming skills, and strong mathematical, statistical, and ecological background.
Desired Qualifications:

Doctorate degree in statistics, ecology, evolution or related discipline. Strong record of research productivity in quantitative methods is required.