- Where
- University of Arkansas, Fayetteville
- Level
- Mid
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Closing Date:
Type of Position:
Research
Workstudy Position:
No
Job Type:
Regular
Work Shift:
Sponsorship Available:
No
Institution Name:
Division of Agriculture of the University of Arkansas The University of Arkansas System Division of Agriculture is a state wide campus, with faculty based on University campuses, at Research and Extension Centers and in every Arkansas county. It consists of the Arkansas Agricultural Experiment Station (AES) and the Arkansas Cooperative Extension Service (CES), and is home to more than 1400 employees. The Division was established in 1959 and is headed by the Vice-President for Agriculture. The Division headquarters is located in Little Rock with the rest of the University of Arkansas System Administration. As an employer, the University of Arkansas System Division of Agriculture offers a vibrant work environment and a workplace culture that promotes a healthy work-life balance.
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Department:
CSES | Crop, Soil and Environmental Sciences
Department's Website:
https://crop-soil-environmental-sciences.uark.edu/
Summary of Job Duties:
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.
Responsibilities
Research (80%)
The successful candidate will be expected to:
• 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…