Clemson University is recruiting a Postdoctoral Research Associate maize genomics and systems genetics to lead maize field-based physiological studies, conduct complementary wet-lab molecular work, and collaborate with teams at Cornell/USDA-ARS and Clemson to develop and apply AI and machine learning approaches for multi-omics integration and gene discovery. The position will be based in the lab of Dr. Rajan Sekhon (sekhon@clemson.edu) in the Department of Genetics and Biochemistry.  

This position is part of an interdisciplinary effort to understand how source sink dynamics shape maize leaf senescence and nutrient partitioning, and to identify the genes and regulatory networks that control these traits. The postdoc will generate and integrate time-resolved field phenotypes (e.g., leaf physiology, developmental timing, senescence progression, and nutrient remobilization) with multi-omics datasets (e.g., single cell transcriptomics, chromatin accessibility, metabolomics, and other molecular profiles). The project will use AI/ML methods to connect phenotypes to molecular states, prioritize candidate genes and regulatory interactions, and nominate testable targets for functional validation across environments.

Responsibilities

• Field research leadership: Design, execute, and analyze maize field trials, including experimental design, planting logistics, phenotyping pipelines, tissue collection, and data quality control.
• Wet-lab molecular work: Coordinate and/or perform sample processing and downstream assays aligned with project goals (e.g., transcriptomics, metabolomics, and gene validation). Experience with single cell omics is a plus.
• AI and ML collaboration: Partner with Cornell and Clemson computational teams to integrate multi-omics and field phenotype data using modern ML/AI methods.
• Team science: Participate in cross-institution meetings and contribute to manuscripts, conference presentations, and collaborative project deliverables.

Qualifications

• PhD in plant biology, genetics, genomics, quantitative biology, or a related field.
• Demonstrated experience with field experiments (maize or other crops strongly preferred).
• Strong molecular biology skills and experience executing wet-lab workflows independently.
• Experience working with biological datasets, including proficiency in R and/or Python, and interest in ML and multi-omics data integration (hands-on ML experience is a plus, but not required).