Postdoctoral Research Associate, Computational Ecology

A 3-year postdoctoral research associate position focused on eco-evolutionary modeling of bat populations affected by White-Nose Syndrome (WNS) is available with the Computational Ecology Lab at the University of Montana (UM). Funded by a grant from the National Science Foundation Ecology and Evolution of Infectious Disease program (The Bat-Fungi Disease & Evolution Project), the postdoctoral research scientist will focus on the disease system WNS in the United States, working with a new computational model for eco-evolutionary epidemiological dynamics across three bat host species. Using this modeling approach, research questions will address how host ecology and evolution, in response to disease and environment, interact to steer disease dynamics on complex changing landscapes. This project will yield a deep understanding of the processes driving disease dynamics and spread by integrating ecological, genomic and epidemiological approaches into a generalizable forecasting model. Understanding the factors affecting pathogen transmission across human-impacted landscapes is essential for future disease control strategies and development.

The postdoctoral research scientist will be part of collaborative international project team between US and UK institutions to model bat evolution and pathogen transmission dynamics in changing landscapes. They will work closely with the US-team leading simulation modeling efforts (Drs. Erin Landguth, Casey Day, UMT) and the UK-team leading the molecular and genomic data analyses (Drs. Orly Razgour, Rhys Farrer, Duncan Wilson, and UK-hired postdoctoral research scientist, University of Exeter). The US-postdoctoral research scientist will also be provided the opportunity for field and lab research working closely with Dr. Julie Weckworth, UMT, as well as state, federal and non-governmental organizations.

The postdoctoral research scientist will have the opportunity to participate in field work, research, teaching, and mentoring activities that will further their career and professional development training. They will serve as lead author on peer-reviewed publications;participate in manuscript review;disseminate results at regional, national and international conferences;and participate in seminar series and outreach events. Conference travel each year has been included in project budgets for this person.

Qualifications:

– PhD (by start date) with experience in one or more of the following: wildlife biology, disease ecology, landscape/spatial ecology, connectivity, population/landscape genetics, simulation modeling, computational biology, or other related experience.
– Experience in data analysis, modeling, and statistical analysis.

Preferred Qualifications:

– Coding proficiency in one or more of the following languages: R, Python, C++, Java, etc.
– Experience with HPC cluster and Cloud computing.
– Expertise in GIS and the processing/analysis of remotely sensed data and familiarity with large data repositories (e.g., Google Earth Engine).
– Expertise in the application and interpretation of spatial statistical models and spatio-temporal modeling, individual-based modeling, and machine learning models.
– Record of research output in high-quality publications.

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