Assistant Professor - Integration of Omics in Plant Breeding

University of Arkansas System•Fayetteville, AR

27d

About The Position

The Assistant Professor will establish a nationally recognized program in molecular plant breeding, agricultural data analytics, and applied computational breeding. The position integrates multi-omics data, statistical genetics, and modern computing approaches to develop actionable tools for plant breeders. The successful candidate will combine technical expertise with the ability to create practical, breeder-friendly solutions and collaborate across crops and disciplines. The role is based in the Department of Horticulture within the Division of Agriculture of the University of Arkansas System. It is a tenure-track faculty position with an 80% research and 20% teaching allocation. The research program will focus on computational breeding, multi-omics integration, AI-assisted decision support, and applied genomics for plant improvement, drawing on the Arkansas High Performance Computing Center and the Division’s statewide research and extension network. Teaching includes annual undergraduate and graduate courses, student mentoring, and curriculum development.

Requirements

Ph.D. in Plant Science, Plant Molecular Biology, Plant Breeding, Genetics, Genomics, Data Science, AI/ML, or a closely related discipline or 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 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 in computational breeding, multi-omics integration, AI-assisted decision support, and applied genomics for plant improvement.
Collaborate with University of Arkansas System 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 for data-driven decision-making.
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 existing computing infrastructure, including the Arkansas High Performance Computing Center, to manage large datasets and implement advanced analytic pipelines.
Collaborate with faculty across departments and contribute to interdisciplinary initiatives that accelerate crop improvement and adoption of modern breeding strategies.
Support and mentor graduate students in applied computational and genomic projects, including hands-on training in 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, and one graduate course in quantitative genetics.
Contribute to curriculum innovation in molecular plant breeding, computational technologies, and agricultural data science.