Position Overview

The Min H. Kao Department of Electrical Engineering and Computer Science (EECS) at The University of Tennessee, Knoxville (UT) 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.

The ideal candidates for this cluster search should have a collaborative mindset and prioritize working with colleagues to realize shared research and educational achievements, including large-scale proposals, joint publications, and new transdisciplinary curricular programming. For early career faculty, the cluster offers a unique framework for professional development and mentorship within a rich transdisciplinary environment. The Knoxville campus of the University of Tennessee is seeking candidates who have the ability to promote an atmosphere where all members of the university community feel welcome and can thrive.

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

Required Qualifications

• PhD degree in Computer Science, Computer Engineering, Data Science, Electrical Engineering, or a related discipline at the time of appointment
• Demonstrate expertise in artificial intelligence, machine learning, or data science
• Strong publication record commensurate with the rank of appointment
• Effective, high-quality teaching skills
• Effectively mentor undergraduate and graduate students

For an Appointment at the Assistant Professor rank:

• Show potential for securing funding for the research programs
• Show potential for participation in interdisciplinary teams

For an Appointment at the Associate Professor rank:

• Expected to have conducted nationally/internationally recognized research works
• Demonstrate strong leadership potential

For an Appointment at the Full Professor rank:

• 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
• Possess the vision and capability to lead major interdisciplinary initiatives that bridge AI/ML with agricultural and forestry applications.

Preferred Qualifications

Previous experience working in the convergent areas of:

• 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
• 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 position is filled. Please submit the following items online in Interfolio to complete your application:

• Cover Letter
• Curriculum Vitae
• Research Statement
• Teaching Statement
• Names and Contact Information of Three References

Apply Now