POSITION TITLE: Post-Doctoral Research Associate
Hiring Organization/Employer: The University of Tennessee, Institute of Agriculture
Application Deadline: June 15, 2026
Education Required: PhD
Location: Knoxville, TN
Description:
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.
Compensation & Benefits:
$63,000
Responsibilities:
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.
Preferred Qualifications:
Education:
o 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.
o The doctoral degree must have been received within the past five years, or by June 15,
2026.
• Experience:
o Demonstrated experience in data analyses and experimental design.
o Demonstrated experience in applying inferential and descriptive statistics from biological
field experiments.
o Experience working with large datasets and/or disparate sets of data.
• Knowledge, Skills, Abilities:
o Knowledge of basic plant biology.
o Ability to collect data using standard procedures to measure tree growth and
morphology.
o Skilled in use of coding or scripts to conduct data analysis with statistical software
packages, such as R or SAS.
o 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:
o Preference will be given to applicants with at least one forestry degree from an Society of
American Foresters accredited program.
o Applicants with course work or degrees in statistics, bioinformatics, data sciences or
closely related fields will be preferred.
• Experience:
o Research and/or work experience in eastern North American hardwood forests.
o Demonstrated experience in using nonparametric, parametric, predictive modelling, and
multivariate statistics to analyze complex datasets from disparate sources or studies.
• Knowledge, Skills, Abilities:
o Knowledge of southern Appalachian hardwood ecosystems and associated regeneration
practices, particularly artificial regeneration.
o Knowledge of basic tree genetics and/or tree improvement.
o Demonstrated skills in transferring science to land managers by synthesizing research
information from various sources into science delivery products.
o Ability to integrate and explore datasets for complex analysis using statistical analyses.
How to Apply: