SHRA Temporary Research Associate

at  University of North Carolina Wilmington

BRIEF SUMMARY OF WORK FOR THIS POSITION

The research associate will work on a project that develops new risk ratings and a risk rating reduction methodology for use in the next version of the U.S. Forest Projects Compliance Offset Protocol (COP). The research associate will use machine learning and a spatially explicit wildfire model and leverage several unique forest datasets to quantify reversal risk at the ecoregion level for the continental US, Alaska, and Hawaii. The research associate will have opportunities to collaborate with scientists in related projects, such as wildfire modeling and fuel treatment simulation.

Specific activities include:

• Produce TreeMap dataset for Alaska and Hawaii using machine learning methods
• Conduct spatially-explicit wildfire modeling (i.e., large-fire simulator (Fsim)) runs under climate change scenarios
• Produce carbon risk functions by integrating various datasets and model outputs (e.g., TreeMap and FSim outputs)
• Establish risk ratings and risk reduction methods for wildfire, insects, and disease
• Write reports and manuscripts

MINIMUM EDUCATION AND EXPERIENCE REQUIREMENTS

B.A. in Science, Technology, Engineering, and Math (STEM). Research experience beyond a class project.

PREFERRED EDUCATION, KNOWLEDGE, SKILLS & EXPERIENCE

Preference will be given to applicants who have

• M.S. in geoscience, geography, environmental science, biology, ecology, or a related field, with a focus on ecological modeling.
• Robust knowledge of ecological processes and principles, ecological modeling, and climate change science.
• Familiarity with ecological and wildfire modeling literature.
• Experience using a fire behavior or process-based ecological model, from calibrating the model to a large study area, running multiple scenarios, and analyzing output.
• Coding skills (e.g., Python, and R) for data analysis and machine learning.
• Technical skills for running fire behavior or ecological models and analyzing output, including the use of a GIS system; and demonstrated ability to make publication-quality charts and maps.
• The ability to author manuscripts suitable for publication in peer-reviewed journals.