PhD Assistantship in Forest Biometrics at University of Tennessee

University of Tennessee

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Description

The School of Natural Resources at the University of Tennessee, Knoxville, is seeking a motivated doctoral candidate to join the Forest Biometrics Lab for the Fall 2026 semester. This position offers a unique opportunity to conduct advanced research while gaining teaching experience in the field of natural resources. Under the supervision of Dr. Tyler Gifford, the selected student will contribute to critical developments in forest modeling and quantitative decision-making.

Research & Responsibilities

The student's research will focus on advancing the field of forest biometrics. Key areas of investigation may include:

• Hardwood Forest Dynamics: Studying the growth and yield of various hardwood species.
• Structural Complexity: Quantifying structural diversity across different species mixtures and silvicultural techniques.
• Quantitative Tools: Creating data-driven decision tools for modern forest managers.
• Professional Engagement: Participating in collaborative meetings, presenting findings at professional conferences, and publishing research in reputable academic journals.

Funding & Benefits

This PhD Assistantship provides a comprehensive support package for the duration of the program:

• Competitive Annual Stipend: Financial support to cover living expenses.
• Tuition Waiver: Full coverage of academic tuition fees.
• Health Insurance: Comprehensive health benefits included.
• Guaranteed Funding: Financial support is secured for three academic years, subject to satisfactory academic and research progress

Candidate Qualifications

Prospective students should meet the following eligibility requirements:

• Education: A Master’s degree from a U.S. university (or equivalent) in forestry, forest biometrics, natural resources, or a closely related field.
• Technical Skills: Proven experience with forest inventory data, growth and yield modeling, or advanced statistical methods.
• Programming: High proficiency in statistical analysis, data management, and statistical programming (specifically R).
• Communication: Strong written and oral communication skills for teaching and publishing.
• Preference: Candidates with a prior track record of publishing in scientific journals are preferred.

Application Process

The lab will review candidates on a rolling basis until the position is filled. Interested individuals should email Dr. Tyler Gifford at tgiffor1@utk.edu with the following materials:

• A brief description of your research interests, career goals, and relevant experience.
• A current Resume or CV.
• Contact information for three professional references.
• Unofficial undergraduate and graduate transcripts