Post-Doctoral Research Associate, UT School of Natural Resources: UTIA

University Of Tennessee College

University Of Tennessee College

Knoxville, TN, USA

USD 63k-63k / year

Posted on Apr 30, 2026

The Postdoctoral Research Associate’s primary responsibility will be to continue existing research on silvicultural studies of various hardwood species throughout the Central Hardwood Region. The experiments were established with pedigreed seed sources and highly characterized seedlings at the time of planting, providing unique opportunities to expand knowledge and refine management recommendations. The research will include statistical analyses of new and existing long-term data collected over years or decades, reevaluation of existing studies, developing manuscripts for publication, and transferring information to land managers and other researchers. Synthesis and integration of disparate datasets, research findings, and inferences are required to move science forward to assist forest managers working in hardwood ecosystems.

This position reports to the Associate Professor of Silviculture and Forest Ecology in the School of Natural Resources, while also collaborating with the University of Tennessee’s Tree Improvement Program (UT-TIP) staff, students, and other partners to accomplish specific goals and objectives. The incumbent will be expected to interact with professors, extant and emeritus, who established the research. The position is intended for a one-year duration with the possibility of annual extension for up to three years, based on performance and continued availability of grant funding.


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The Postdoctoral Research Associate’s primary responsibility will be to continue existing research on silvicultural studies of various hardwood species using existing and new data sets. Research activities will include statistical analyses of long-term data , reevaluation of old and new studies, developing manuscripts for publication, and transferring information to land managers and other researchers.

Required Qualifications

  • Education:
    • The candidate should be currently pursuing or have received a doctoral degree in forestry, natural resources, plant ecology, plant sciences, or a closely related field.
    • The doctoral degree must have been received within the past five years, or by June 15, 2026.
  • Experience:
    • Demonstrated experience in data analyses and experimental design.
    • Demonstrated experience in applying inferential and descriptive statistics from biological field experiments.
    • Experience working with large datasets and/or disparate sets of data.
  • Knowledge, Skills, Abilities:
    • Knowledge of basic plant biology.
    • Ability to collect data using standard procedures to measure tree growth and morphology.
    • Skilled in use of coding or scripts to conduct data analysis with statistical software packages, such as R or SAS.
    • Ability to work productively and collaboratively with teams, and independently.

Applicants must be legally authorized to work in the United States on a full-time basis without need now or in the future for sponsorship for employment-based visa status.

Preferred Qualifications

  • Education:
    • Preference will be given to applicants with at least one forestry degree from an Society of American Foresters accredited program.
    • Applicants with course work or degrees in statistics, bioinformatics, data sciences or closely related fields will be preferred.
  • Experience:
    • Research and/or work experience in eastern North American hardwood forests.
    • Demonstrated experience in using nonparametric, parametric, predictive modelling, and multivariate statistics to analyze complex datasets from disparate sources or studies.
  • Knowledge, Skills, Abilities:
    • Knowledge of southern Appalachian hardwood ecosystems and associated regeneration practices, particularly artificial regeneration.
    • Knowledge of basic tree genetics and/or tree improvement.
    • Demonstrated skills in transferring science to land managers by synthesizing research information from various sources into science delivery products.
    • Ability to integrate and explore datasets for complex analysis using statistical analyses.

Application Instructions:

To express interest, please apply with the noted below attachments. To be assured of full consideration, completed applications with all requested materials should be submitted on or before May 31, 2026.

  • Resume
  • Cover letter

Work Location:

  • Primary Location: The University of Tennessee, Knoxville, School of Natural Resources.
  • Secondary Location: University of Tennessee’s Ames AgResearch and Education Center.

Compensation and Benefits:

  • UT market range: PG00
  • Anticipated hiring range: $63,000
  • Find more information on the UT Market Range structure here
  • Find more information on UT Benefits here

About The School pf Natural Resources:

  • The University of Tennessee is one of two land grant institutions in the state and is the state’s public flagship university. The School of Natural Resources is the comprehensive natural resources program in the state, consisting of 38 teaching, research, and Extension faculty; 18 professional and 8 administrative staff members; and more than 350 students. The school’s mission is to advance the science and sustainable management of natural resources to promote their health, use, and appreciation in Tennessee, the region, and beyond through programs in teaching, research, and Extension.

The successful candidate will:

  • Help organize, prioritize, analyze data, and collect data from existing studies, explore new research questions or hypotheses, and synthesize scientific information to benefit the UT-TIP and forest land managers who plant hardwood seedlings in the Central Hardwood Region.
  • The incumbent will collaborate with the UT-TIP research and program team to help refine goals and objectives and expand the scope for analysis of existing studies.
  • The successful candidate will be expected to perform appropriate statistical analyses, both exploratory and priori hypothesis testing, to develop manuscripts for publication in refereed journals, and deliver science through outreach publications and presentations at scientific meetings and workshops.