Postdoctoral Scholar, Wildfire Science and Technology Lab

The University of Nevada, Reno (UNR) appreciates your interest in employment at our growing institution. We want your application process to go smoothly and quickly. Final applications must be submitted prior to the close of the recruitment.

If you need assistance or have questions regarding the application process, please contact our recruitment helpline at (775) 784-1495 or jobs@unr.edu. For UNR Med professional job postings, please contact the Office of Professional Recruitment at (775) 784-6778. 

The newly established Wildfire Research and Technology Lab at University of Nevada, Reno invites applicants for multiple postdoctoral fellows to conduct research in technological and scientific aspects of Wildfire Science and Engineering across the Pre-Fire, Active-Fire, and Post-Fire domains. Our goal is to develop new capabilities to effectively prepare for, respond to, and recover from wildfires. We seek dynamic individuals with knowledge of fire science (or related fields) who are willing to work within a multidisciplinary team in a fast-paced research and development environment, learn new skills, and produce high-quality research products. The positions are part of a cluster hire in support of the new Lab. The candidates will interact with other research teams at the Lab as well as faculty members at UNR and other partnering institutes and agencies. The Lab is committed to postdoctoral mentorship and supports independent career development for successful candidates.   

For Pre-Fire domain, we seek candidates with strong background in engineering risk, reliability, and stochastic simulation concepts with experience in wildfire modeling and loss assessment and regional planning of communities at the wildland-urban interface (WUI). Successful candidates will contribute to the development of solution platforms for integrating spatial and temporal information, harnessing remote sensing data, using climate information, understanding fuel accumulation and running physics-based or data-driven wildfire simulations to design and assess scenarios for risk mitigation.

For Active-Frie problem domain, we seek candidates with strong background in analytical and computational methods related to physics-based wildfire simulation, fuel modeling, remote sensing, data-driven methods, artificial intelligence, and real-time applications. The successful candidates will contribute to the development of faster than real-time data and modeling tools for emergency response management and decision support.

In the post-fire domain, candidates will establish quantitative links between wildfire behavior with post-fire hydro-ecological consequences affecting landscapes and water bodies to quantify impacts and establish boundary conditions for recovery modeling. Specifically, we seek capable individuals with proficiency in modeling to assess how post-wildfire impacts soil functioning (infiltration, runoff, strength, composition, ecology), smoke and pyrometeor deposition, soil carbon and nutrient cycle alterations, sediment transport, impacts of altered vegetation, soil and hydrology on landscape functions to guide targeted restoration strategies for accelerating recovery of fire-affected landscapes.

Required Qualifications

• Ph.D. in Engineering, Physics, Ecology and Forestry, Earth Science, Computational Science, or related fields.

• Ph.D. research experience in wildfire engineering, fire science, landscape modeling, or related topics.

• Experience in working with big data.

• Excellent English-language communication skills (oral and written).

• Demonstrated ability to perform research and publish results in peer-reviewed literature.

• Completion of a doctoral degree in the appropriate discipline is required.

• The doctoral degree must have been completed within the five years immediately preceding the first date of appointment as a postdoctoral fellow at the University.

• The individual cannot have held previous positions in the professional ranks.

Preferred Qualifications

Candidates who possess the following skills and attributes are encouraged to apply:

• Background in large-scale numerical modeling (regional planning, ecological, hydr