Remote Twig Jobs (NOW HIRING)

The New Mexico Forest and Watershed Restoration Institute (NMFWRI) is seeking a full-time (100% FTE) postdoctoral scholar with expertise in fire modeling and remotely sensed data to support innovative research. This project will improve our understanding of the conditions under which fuel treatments effect wildfire behavior and to evaluate long term post fire impacts within the Hermit's Peak Calf Canyon Fire burn scar.
As NMFWRI is part of the Southwest Ecological Restoration Institutes (SWERI), The postdoctoral scholar will join a collaborative team of researchers, on the grant funded ReSHAPE project https://reshapewildfire.org/. The researcher will incorporate fuel treatment databases currently being developed as part of the national Treatment and Wildfire Interagency Geodatabase (TWIG). This researcher will leverage existing spatial data on landscapes, fire behavior, and fuel treatments to evaluate real-world wildfire-treatment encounters across diverse U.S. landscapes. The researcher will work closely with the staff of the three SWERIs to coordinate research using TWIG to ensure data quality and specificity is additive to potential uses, end users and analyses.
The incumbent will be responsible for processing and analyzing large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., TWIG, FACTs, FTEM, field data) both locally with R/Python and via Google Earth Engine for treatment outcome research. Work will include analysis of spatial and related data (vector, raster, imagery) sufficient to support multi-scale and/or multi-resource assessments and monitoring. Knowledge of data and data management sufficient to create, transform and integrate data in a variety of resolutions and formats is necessary. Analysis will include running machine learning algorithms (e.g., Random Forest, CART) and regression models to derive ecological insights from big data sets. The project entails developing reproducible and scalable methodologies, using common software and programming languages, that can be used by land managers for decision making support.
We take care of our own!
Once hired, our Spatial Analyst Post Doc will be mentored by experienced GIS professionals and have a chance to teach us a thing or two as well! They will have many opportunities for professional development such as attending conferences and presenting their research. They will work with a passionate team engaged in and excited about education, ecological monitoring, and collaborative conservation.
As a New Mexico Highlands University employee, benefits include superb health, paid leave, and retirement benefits, an extended winter holiday break, and tuition waivers at New Mexico Highlands University.
Where you will work.
NMFWRI's Spatial Analyst Post Doc will have the option for hybrid /remote work but must be willing to travel to New Mexico on a quarterly basis and attend regular virtual (zoom) meetings. In-state and out-of-state travel will be required, including attending conferences and regional meetings. Approved travel costs will be reimbursed.
DUTIES AND RESPONSIBILITIES:
The incumbent will be responsible for processing and analyzing large remote sensing datasets (e.g., Landsat, Sentinel-2, MODIS) and spatial datasets (e.g., TWIG, FACTs, FTEM, field data) both locally with R/Python and via Google Earth Engine for treatment outcome research.
Work will include analysis of spatial and related data (vector, raster, imagery) sufficient to support multi-scale and/or multi-resource planning, assessments, and monitoring. Knowledge of data and data management sufficient to create, transform and integrate data in a variety of resolutions and formats.
Project management, leading analysis, modeling, and visualization efforts, and coordinating project communication.
Use of project management software to track project tasks (e.g. GitHub)
Prepare and submit manuscripts for publication in scholarly journals
Work successfully in a team environment and collaborate effectively with other research partners.
Prepare, deliver and contribute to the production, communication, and publication or dissemination of high-quality science-based products for use by scientist, managers and/or collaborative forestry groups
PHYSICAL DEMANDS:
Standing Frequently
Sitting Frequently
Walking (cross country) Infrequently
Bending Infrequently
Squatting Infrequently
Kneeling Infrequently
Lifting (30lbs or less) Infrequently
EDUCATION:
PhD in forestry, ecology, natural resources, wildland fire science, or geography.
EXPERIENCE:
More than 2 years programming experience using software such R, and R Studio for spatial data processing and analysis.
More than 1 years programming experience using Google Earth Engine for spatial data processing and analysis.
Experience automating spatial analysis workflows with remote sensing, multiple data types (spreadsheets, databases, raster and vector spatial data), big data, or spatial analysis across multiple software platforms.
Evidence of expertise in fire behavior and/or fire management in the western US
Evidence of expertise or experience using geospatial data analytics and products.
Evidence or experience in collaborating, motivating and encouraging staff to perform at a high level
Evidence of professional oral communication to diverse audiences.
Demonstrated research accomplishments and peer-reviewed publications.
Evidence of personal or professional commitment to diversity as demonstrated by persistent effort, active planning, allocation of resources and/or accountability.
Experience with fire behavior modeling programs (e.g., FlamMap, FSIM, etc).
Evidence of supervision of others in collaborative project settings
Knowledge of western US forest and fire ecology, wildfire management, and/or wildfire experience.
Experience with cloud/ cluster computing to fit large models.
Preferred Skills
Expertise in GIS, remote sensing, and statistics and programming proficiency in R, Python, Google Earth Engine or similar languages.
Background in natural resource management applications and wildland fire sciences.
Experience processing and analyzing large remote sensing datasets.
Expertise in fire science, fire behavior models, and fuel mapping.
Working knowledge of forest or ecosystem dynamics and disturbance ecology
Proficient with running machine learning algorithms (e.g., Random Forest, CART) and regression models to derive ecological insights from big data sets.
Strong interpersonal and communication skills.