The Department of Forest, Rangeland and Fire Sciences (FRFS) and College of Natural Resources (CNR) at the University of Idaho invites applications for a Research Assistant Professor focused on Forest Inventory and Geospatial Data Analytics. Specifically, we seek an individual with expertise in 3-D and geospatial data analytics and applied statistics for forest inventory and forest biomass applications. The position will work jointly with forest industry and natural resource management agencies to develop, apply, and validate geomatic, statistical, and exploratory geospatial analysis based upon forest growth models and software. It is expected that the individual will work closely with current faculty and research groups in FRFS and CNR (e.g., Intermountain Forestry Cooperative and University of Idaho Experimental Forest) to help expand our interdisciplinary efforts focused on forest supply chain dynamics, including research centered on high resolution geospatial data, digital inventory, and forest growth modeling. The position is a term position contingent on continuation of funding.
Required qualifications include: 1) completed Ph.D. in Forestry or related field with an emphasis on forest biometrics, forest growth modeling, geospatial data analytics, and or statistical modeling; 2) excellent oral and written communication skills and experience publishing in peer- reviewed journals; 3) record of success in acquiring research funding from external sources; and 4) demonstrated ability to work in a team environment.
Preferred qualifications include: 1) demonstrated expertise in remote sensing for forestry applications; 2) demonstrated experience working with LiDAR data for forestry and forest biomass applications including flight and acquisition planning, processing of point-cloud data and summary raster metrics, and validation/accuracy assessment of the results; 3) proficiency with current forest inventory practices and an understanding of forest supply chains; 4) demonstrated experience creating and interpreting complex statistical models, with a focus on the validation of remotely sensed products; 5) demonstrated experience with statistical modeling and proficiency with different programming languages, such as Python, C/C++, R, and SAS; 6) strong data visualization skills; and 7) experience advising graduate and/or undergraduate research projects.