We seek a postdoctoral research fellow for an exciting new project investigating the ecological mechanisms by which climate change alters patterns and outcomes of human–wildlife interactions. The successful candidate will assist with 1) integrating data and models on climate change, animal distributions, and human demography to predict human–wildlife interactions at multiple spatial scales; 2) organizing and running a Synthesis Working Group on a topic related to their research; and 3) writing scientific, peer-reviewed publications. The position is a two(2) year term with the possibility of renewal pending performance and funding. The position is expected to start Fall 2022; however, the official start date is negotiable.
The postdoctoral scholar will be advised by Dr. Neil Carter and located at the University of Michigan’s School for Environment and Sustainability (SEAS, Ann Arbor campus). The successful candidate will be a part of the University of Michigan’s Institute for Global Change Biology (IGCB)—a nexus for interdisciplinary global change research. The candidate will work closely with colleagues at the University of Michigan (Drs. Brian Weeks and Jacob Allgeier) and the University of Washington (Dr. Briana Abrahms) as part of a IGCB Working Group titled “Forecasting climate-driven changes in human–wildlife interactions.”
• Develop a geospatial framework for integrating future global distributions of wildlife and people under various climate scenarios.
• Develop models (statistical or simulation) of climate-influenced human–wildlife interactions at site and regional scales.
• Produce and evaluate spatial predictive surfaces of future hotspots of human-wildlife encounters.
• Work in an interdisciplinary team and help build an analytical framework for future applications.
• Lead and collaborate on writing scientific manuscripts.
PhD (by start time of position) in Conservation, Data Science, Ecology, Geography, Wildlife Sciences or related field, from an accredited university. Competitive applicants will have strong numerical and statistical modeling background and experience with computer programming and coding (e.g., R, Python), GIS and remote sensing applications, analyzing large datasets, and working in collaborative teams.
Preference will be given to candidates who have experience integrating climate projections into their workflows, as well as experience with species distribution modeling, hierarchical modeling, and/or simulation modeling. Strong written and verbal communication skills and a demonstrated ability to work both in a team environment and independently are required. Preference will also be given to scholars with a proven publication record.