"Tenure-Track Positions in AI/ML for Agriculture and Forestry Systems Assistant, Associate, or Full Professor - Fall 2026"

Job Details

Tenure-Track Positions in AI/ML for Agriculture and Forestry Systems Assistant, Associate, or Full Professor - Fall 2026 (1/2)

Location: Knoxville, TN, United States

Open Date: Sep 3, 2025

Description:

The Min H. Kao Department of Electrical Engineering and Computer Science (EECS) at The University of Tennessee, Knoxville (UTK) is seeking candidates for two tenure-track faculty positions at the assistant, associate, or full professor level in artificial intelligence and machine learning (AI/ML) with applications to agriculture and forestry systems. These positions are part of strategic cluster hires in the Resilient Agriculture and Forestry Systems (RAFS) and Plant Ecosystem Resilience in a Changing Environment (PERCE) clusters, which aim to address critical challenges in food security and environmental sustainability through data-driven solutions.

We seek exceptional candidates with expertise in AI/ML who can develop innovative computational solutions to address challenges in agricultural and forestry systems. Areas of particular interest include, but are not limited to: precision agriculture technologies, remote sensing and geospatial imaging, multi-sensor data fusion, IoT and sensor networks for environmental monitoring, autonomous agricultural systems, disaster risk assessment and mitigation, predictive modeling, and data analytics for agricultural decision support systems.

Candidates will be expected to (1) establish and maintain an internationally recognized, externally funded research program; (2) actively participate in interdisciplinary collaborations within the RAFS/PERCE clusters and across the university; (3) publish high-impact scholarly research; (4) teach undergraduate and graduate courses in computer science, computer engineering, or electrical engineering; (5) mentor graduate and undergraduate students; and (6) contribute to departmental, college, and university service.

Research Context and Opportunities

The RAFS and PERCE clusters represent a major institutional investment in sustainable and resilient agriculture and forestry systems. Faculty hired through this search will have access to: Ten UT AgResearch and Education Centers across Tennessee, providing real-world testbeds for agricultural technology deployment; State-of-the-art computing resources, including high-performance computing clusters and GPU resources for AI/ML research; Strong partnerships with Oak Ridge National Laboratory, including access to supercomputing facilities and collaborative research opportunities; The AI Tennessee Initiative, providing interdisciplinary connections and resources for AI research and education; Collaborative opportunities with faculty across EECS, Biosystems Engineering and Soil Science (BESS), Entomology and Plant Pathology (EPP), Forestry, Wildlife and Fisheries (FWF), and other departments; Access to extensive agricultural and forestry datasets from statewide research stations and industry partners.

Qualifications:

Applicants must hold a Ph.D. in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or a closely related field at the time of appointment. Candidates should demonstrate expertise in artificial intelligence, machine learning, or data science, with a strong publication record commensurate with the rank of appointment. For an Appointment at the Assistant Professor rank, the candidate is expected to demonstrate potential for securing funding for research programs and for participating in interdisciplinary teams. The candidate is also expected to demonstrate potential for effective, high-quality teaching skills and the ability to effectively mentor undergraduate and graduate students. For an Appointment at the Associate Professor rank, the candidate is expected to have conducted nationally/internationally recognized research and demonstrate strong leadership potential. The candidate is also expected to demonstrate effective and high-quality teaching skills, as well as the ability to effectively mentor undergraduate and graduate students. For an Appointment at the Full Professor rank, the candidate is expected to demonstrate established leadership in their field with a strong track record of funded research, high-impact publications, and successful mentorship of junior faculty and students. The candidate should possess the vision and capability to lead major interdisciplinary initiatives that bridge AI/ML with agricultural and forestry applications. Preferred qualifications include demonstrated experience applying AI/ML to agricultural, forestry, or environmental systems; track record of interdisciplinary collaboration; experience with grant funding from agencies such as NSF, USDA, DOE, or similar; industry partnerships or technology transfer experience; and familiarity with agricultural production systems, forest ecosystems, or related fields.

Application Instructions:

The application deadline is November 14, 2025. Applications received after the deadline may be considered until the positions are filled. Please submit the following items online via Interfolio to complete your application: Cover Letter addressing your interest in the position and fit with the RAFS/PERCE clusters, Curriculum Vitae, Research Statement describing your research vision and how it aligns with AI/ML for agriculture and forestry systems, Teaching Statement including teaching philosophy and potential course contributions, Names and contact information of three references. For questions about the position, please contact the Search Committee Chair, Dr. Charles Cao at cao@utk.edu, or Co-Chair, Dr. Hector Santos-Villalobos at hsantosv@utk.edu.

Equal Employment Opportunity Statement:

All qualified applicants will receive equal consideration for employment and admission without regard to race, color, national origin, religion, sex, pregnancy, marital status, sexual orientation, gender identity, age, physical or mental disability, genetic information, veteran status, and parental status, or any other characteristic protected by federal or state law.