Job Type: On-site, postdoc
Location: Northern Arizona University - Flagstaff, Arizona
Pay: $58,882/year
Application Deadline: April 20, 2026
Description:
This position is an on-site position which requires the incumbent to complete their work primarily at an NAU site, campus, or facility with or without accommodation. Opportunities for remote work are rare.
This position is subject to the availability of funding. The incumbent is not eligible for Service Professional non-renewal notice, or Classified Staff layoff or recall status.
Driving a vehicle on behalf of the university is anticipated to be a regular part of this position. Arizona Administrative Code Fleet Safety Policy requires all employees who drive on university business become authorized by submitting Driver’s license information for driving record monitoring, and completion of training appropriate to the level of driving performed. The law applies to all faculty, staff, and students who drive personal or university-owned motorized vehicles for any business purpose. More information on the NAU Authorized Driver Policy can be found on the NAU website.
The Ecological Restoration Institute, working in collaboration with the Rocky Mountain Research Station, seek a Postdoctoral Scholar to develop next-generation modeling tools that simulate a predefined range of silvicultural and fuels treatments and serve as the foundation for spatially explicit vegetation dynamics projections over 10–20 year planning horizons.
This position will lead the development of next-generation forest modeling tools that represent treatment-driven vegetation dynamics using spatially explicit data intended to bridge forest growth and fuels modeling with point cloud-derived tree lists and advanced three-dimensional fire behavior simulation frameworks. Outcomes are expected to support integrated analysis of vegetation change, fuels, and fire behavior. The Postdoctoral Scholar will build on existing growth and yield frameworks (e.g., baseline Forest Vegetation Simulator growth) and extend them using spatial context, neighborhood interactions, and treatment rules to better represent post-treatment forest trajectories relevant to restoration, fuels reduction, and wildfire resilience. The resulting models are intended to support spatially explicit projections of forest structure and fuels and to provide compatible inputs for three-dimensional fire behavior modeling frameworks such as QUIC-Fire, WFDS, and FIRETEC.
The appointment is a full-time, mentored research position designed to develop the Postdoctoral Scholar as an independent scientist with expertise at the interface of forest ecology, quantitative modeling, and open-source software development.
The position is a full-time Postdoctoral Scholar appointment, renewable annually subject to funding availability and performance, consistent with Arizona Board of Regents policies. The position may be eligible for on-site, hybrid, or remote work arrangements depending on project needs and institutional guidelines.
The Postdoctoral Scholar will be embedded within a collaborative research environment focused on applied forest restoration, fuels management, and wildfire resilience, with strong links to remote sensing, modeling, and decision-support tool development.
Responsibilities:
60% - Research and Tool Development
Design and implement algorithms to simulate silvicultural and fuels treatments within a spatially explicit modeling framework.
Extend baseline growth and yield models by incorporating neighborhood- and competition-based modifiers.
Develop reproducible, well-documented code suitable for long-term research and operational use.
Integrate modeled outputs with spatial forest structure data derived from remote sensing or inventory sources.
20% - Analysis, Validation, and Synthesis
Evaluate model behavior under alternative treatment and growth assumptions.
Assess sensitivity and uncertainty associated with neighborhood-based growth modifications.
Compare simulated outcomes with empirical or literature-based expectations where possible.
15% - Dissemination and Scholarly Output
Publish peer-reviewed manuscripts describing model development, methods, and applications.
Produce technical documentation, example workflows, and open-source repositories to support reuse.
Present research results at scientific meetings, workshops, and partner briefings.
5% - Mentoring and Collaboration
Collaborate with faculty, research staff, and graduate students across forestry, ecology, and remote sensing.
Provide limited mentoring or technical guidance to students or staff engaged in related efforts.
Minimum Qualifications:
PhD or equivalent doctorate in forestry, forest ecology, forest biometrics, quantitative ecology, or a closely related field.
Demonstrated experience with forest growth, competition, or vegetation dynamics modeling.
Strong quantitative skills and experience implementing models in a programming environment (e.g., R, Python, C++, or similar).
Ability to design, document, and maintain reproducible research workflows.
*A combination of related education, experience, and training may be used as an equivalent to the above Minimum Qualifications.
Preferred Qualifications:
Experience working with or extending forest growth and yield models (e.g., FVS or similar frameworks).
Experience working with point cloud-derived treelists and approximations of forest structures.
Familiarity with spatially explicit or individual-based forest simulation models.
Experience integrating field data, inventory data, or remote sensing–derived forest structure into models.
Prior open-source software development experience, including version control and collaborative workflows.
A record of peer-reviewed scientific publication.
Familiarity with western U.S. forest systems and silvicultural practices.
Knowledge, Skills, and Abilities:
Knowledge
Forest ecology, silviculture, and stand dynamics.
Concepts of competition, neighborhood effects, and treatment-driven forest change.
Principles of model design, validation, and uncertainty assessment.
Skills
Scientific programming and algorithm development.
Spatial data analysis and handling of large datasets.
Technical writing and documentation for scientific and applied audiences.
Project organization and coordination within collaborative research teams.
Abilities
Ability to work independently while engaging productively with collaborators
Capacity to translate ecological concepts into computational representations.
Strong problem-solving and analytical reasoning skills.
Effective communication of complex methods and results.
