Employer Colorado State University Job Description, Responsibilities and Required Qualifications or Skills The forestry program in the Department of Forest and Rangeland Stewardship at Colorado State University, through collaboration with other programs in our college and university, generates and communicates knowledge of broad areas of forest science and stewardship to students, managers, policy makers, peers, and the public. We strive to be a preeminent authority in forest science and stewardship through a well-rounded, interdisciplinary faculty encompassing broad expertise in biophysical, management, and social sciences. We manage programs, facilities, and technologies for applied, hands-on learning and problem-solving reaching broad audiences, from matriculated and continuing education students, researchers, forest stewardship professionals, and the general public.This position will be based in Fort Collins, a small but growing city nestled against the eastern flanks of the central Rocky Mountains, approximately one hour north of Denver in north-central Colorado. Its location and Colorado’s idyllic climate make Fort Collins a mecca for outdoor enthusiasts. Within easy reach there are a myriad of trails for hiking, mountain biking, wildlife watching, horseback riding, and back-country skiing on a plethora of public lands, including nearby National Parks, Forests, Grasslands, and countless local natural areas. Fort Collins also has a vibrant cultural scene, with many options for enjoying theater productions, music events, annual festivals, museums, art galleries, restaurants, gardens, and more.We seek an outstanding postdoctoral researcher to conduct research on new models integrating terrestrial plant biodiversity, ecosystem productivity, hyperspectral remote sensing and airborne LiDAR to test prominent hypothesis of the effect of tree diversity on ecosystem productivity. The primary objectives for this postdoctoral position are to 1) Develop models to predict plant functional traits from hyperspectral imagery in forest sites from the National Ecological Observatory Network (NEON), 2) Develop statistical models to assess the effect of remotely-sensed diversity metrics on three forest types: evergreen, deciduous, and mixed, 3) Test prominent hypotheses of diversity-productivity relationships to infer the ecological mechanisms driving these relationships in temperate forest ecosystems. Duty/Responsibility:Research: (Percentage Of Time: 85)Develop machine learning models to predict plant traits from hyperspectral imagery using public open data from NEON sites across a climatic gradient in continental USA.Estimate functional diversity indices from hyperspectral imagery from NEON sites at a local scaleObtain public data on tree growth over time to estimate net primary productivity over timeFit structural equation models to test the effects of remotely sensed biodiversity (from hyperspectral and LiDAR) on forest productivity across three forest types (evergreen, deciduous, mixed forest) from NEON sites.Apply these models to infer ecological mechanisms driving biodiversity-productivity relationships in temperate forest ecosystemsProject management (percentage of time: 15)Ensure project objectives are met by meeting regularly with project supervisor and team, setting interim goals and submitting annual reportsThe postdoc will explore opportunities for continuing the project through future grant proposals Required job applications: Applicants must have completed a Ph.D. in ecology, natural resources, remote sensing, ecosystem ecology or a related discipline, and have:Expertise in processing optical remote sensing data over large areas and using machine learning models for predicting ecological metricsStrong background in remote sensing in hyperspectral imaging using software and programming languages such as R, Python, Google Earth Engine, etc.Demonstrated ability in integrating ecological theory with ecosystem ecology and remote sensing to improve predictions of forests responses to climate changeDemonstrated evidence of excellent written and oral communication skills and organizational abilityDemonstrated proficiency with statistical analyses using open-source softwareDemonstrated evidence of publishing in the peer-reviewed literaturePreferred Job Qualifications: Competitive candidates will have:A background in hyperspectral remote sensingExperience in using machine learning to predict plant metrics, including plant chemistry, from hyperspectral imageryExperience developing statistical models to predict ecosystem yields, forest productivity or other ecosystem properties.Experience working collaboratively in groups.Background in plant functional ecology is strongly desired but not requiredBackground and experience in airborne LiDAR or terrestrial LiDAR are an asset. Job Field: Forestry GIS/Technology Community Forestry & Arboriculture Job Type: Full Time Location Detail: Fort Collins, CO (Hybrid or remote arrangements may be considered, but in-person presence in Fort Collins, CO is preferred.) Salary: $55,000 - $65,000 Job Benefits: How to apply:Follow the direct link to apply online and include all required documents.Applicants must meet the minimum qualifications in the announcement to be considered for hire. Apply no later than September 08, 2025 for full consideration.Mailed or emailed applications will not be accepted.Upload each of the items below individually as a Word Document (.doc), PDF (.pdf), or Rich Text Format (.rtf). Please note that incomplete applications cannot be considered. Please remove social security numbers and birth dates from application materials.A complete application consists of:A cover letter which addresses how professional experience aligns with identified required and preferred qualifications of the position.A current Curriculum Vitae (CV).The names, e-mail addresses, and telephone number of three (3) professional references. References will not be contacted without prior notification to candidates.CSU is committed to full inclusion of qualified individuals. If you are needing assistance or accommodations with the search process, please reach out to the listed search contact Sandra Duran, Sandra.Duran@colostate.eduPlease note, applicants may redact information from their application materials that identifies their age, date of birth, or dates of attendance at or graduation from an educational institution. Application Deadline: Mon, 09/15/2025 - 12:00pm Link to Full Job Posting: https://jobs.colostate.edu/postings/165751
