Assistant Professor - Integration of Omics in Plant Breeding

University of Arkansas • Fayetteville, AR

25d

About The Position

The Assistant Professor will establish a nationally recognized research program in molecular plant breeding, agricultural data analytics, and applied computational breeding. The position focuses on integrating multi-omics data, statistical genetics, and modern computing approaches to provide practical tools for plant breeders. The successful candidate will develop breeder-friendly solutions and foster collaborations across crops and disciplines.

The position is based in the Horticulture Department of the Arkansas Agricultural Experiment Station. The appointment is divided between research (80%) and teaching (20%). Research includes developing an extramurally funded program in computational breeding, multi-omics integration, AI-assisted decision support, and applied genomics; collaborating with University of Arkansas System Division of Agriculture plant breeders; and translating quantitative genetics and statistical approaches into practical platforms and workflows. Teaching includes mentoring graduate students, teaching undergraduate and graduate courses annually, and contributing to curriculum innovation.

The position offers access to the Arkansas Agricultural Experiment Station's statewide research and extension network and the Arkansas High-Performance Computing Center. The candidate will also have opportunities to collaborate with faculty and breeding programs across crops and disciplines.

Requirements

• Ph.D. in Plant Science, Plant Molecular Biology, Plant Breeding, Genetics, Genomics, Data Science, AI/ML, a closely related discipline, or a highly related field; the degree must be conferred by the date of hire.
• Evidence of scholarly productivity in plant breeding and quantitative trait analysis, genomic breeding tools, statistical genetics, or data analytics.
• Excellent verbal and written communication skills.
• Demonstrated interpersonal skills and ability to collaborate with multidisciplinary teams and applied breeding programs.

Nice To Haves

• Experience applying and translating plant production and plant breeding programs and information to support decision-making and adoption of modern breeding strategies.
• Experience in team collaboration and leadership.
• Demonstrated effective grantsmanship and ability to secure competitive funding.
• Experience working with diverse audiences and stakeholders.
• Demonstrated commitment to student training and development, and teaching skills that facilitate learning in undergraduate and graduate students.

Responsibilities

• Develop and implement an extramurally funded research program focused on computational breeding, multi-omics integration, AI-assisted decision support, and applied genomics for plant improvement.
• Collaborate with Division of Agriculture plant breeders to modernize selection pipelines using approaches such as genomic prediction, GWAS, QTL mapping, metabolomics, and high-throughput phenotyping.
• Translate quantitative genetic and statistical approaches into breeder-friendly platforms, apps, dashboards, and analytical workflows.
• Secure extramural funding from federal agencies, commodity boards, interdisciplinary initiatives, and industry partnerships.
• Publish research in peer-reviewed journals, present at scientific conferences, and communicate results to stakeholders and breeding teams.
• Use the Arkansas High Performance Computing Center to manage large datasets and implement advanced analytic pipelines.
• Collaborate with faculty across departments on interdisciplinary crop-improvement initiatives.
• Support and mentor graduate students in applied computational and genomic projects, including data analysis, workflow optimization, and experimental design.
• Teach one undergraduate course annually in plant genetic resources, crop evolution, or a related area aligned with plant breeding applications.
• Teach one graduate course annually in quantitative genetics.
• Contribute to curriculum innovation related to molecular plant breeding, computational methods, and agricultural data science.

Benefits

• Benefits eligible; the Division offers a vibrant work environment and workplace culture that promotes a healthy work-life balance.
• Access to the Arkansas Agricultural Experiment Station's statewide network of research and extension stations.
• Access to the Arkansas High-Performance Computing Center.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree