Assistant Professor

University of Arkansas System•Fayetteville, NC

40d•Onsite

About The Position

As part of a cluster hire in multi-omics integration and data analytics applied to plant breeding, the University of Arkansas Division of Agriculture invites applications for a 12-month, tenure-track Assistant Professor position (80% research, 20% teaching) in Phenomics Applied to Plant Breeding. This is a unique opportunity to join a coordinated cohort of established and new faculty working to modernize and transform public breeding programs across Arkansas. The successful candidate will develop a comprehensive research program focused on high-throughput phenotyping, AI and machine learning-based trait extraction, and data-driven selection strategies that accelerate genetic gain and strengthen applied breeding pipelines across multiple crops.

Requirements

Ph.D. in Plant Science, Agricultural Engineering, Remote Sensing, Data Science, AI/ML, Plant Breeding, or a closely related discipline.
Evidence of scholarly productivity in phenomics, sensing systems, machine learning, computer vision, quantitative trait analysis, or related fields.
Strong communication skills and ability to collaborate with multidisciplinary teams.
Proof of legal authority to work in the United States on the first day of employment.

Nice To Haves

Experience applying phenomics tools in plant breeding.
Experience with UAV operations, sensor integration, robotics, computer vision pipelines, or geospatial analytics.
Demonstrated ability to secure competitive funding.
Demonstrated commitment to student training and development.

Responsibilities

Develop a cutting-edge research program focused on high-throughput phenotyping, image-based trait extraction, sensor integration, and advanced data analytics applied to plant breeding.
Utilize and innovate in sensing platforms including UAVs, robotics, ground-based systems, multispectral and hyperspectral imaging, LiDAR, satellite systems, automated greenhouse phenotyping, and related technologies.
Apply AI, machine learning, statistical modeling, and computer vision to quantify complex traits, improve prediction accuracy, and enable earlier and more precise selection decisions.
Collaborate directly with breeders in soybean, rice, fruits, vegetables, and wheat to integrate phenomics outputs into active selection pipelines.
Establish interdisciplinary partnerships with weed scientists, soil scientists, agronomists, plant pathologists, and agricultural engineers to expand phenotyping applications.
Secure extramural funding from federal agencies (USDA-NIFA, NSF, DOE, NASA, etc.), commodity boards, interdisciplinary initiatives, and industry partnerships.
Publish research findings in peer-reviewed journals, present at scientific conferences, and communicate results to stakeholders and breeding teams.
Teach one undergraduate and one graduate course annually in remote sensing, phenomics, data analytics, machine learning, or a related area aligned with plant breeding applications.
Mentor M.S. and Ph.D. graduate students and engage undergraduate students.
Contribute to curriculum innovation aligned with emerging sensing, computational, and data science technologies in agriculture.