USDA-ARS SCINet/AI-COE Postdoctoral Fellowship in AI-Driven Maize-Fungal Pathogen Genomics

About the role

The opportunity is with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), specifically the Corn Insects and Crop Genetics Research Unit in Ames, Iowa, and the National Center for Agricultural Utilization Research in Peoria, Illinois. ARS is USDA’s chief scientific in‑house research agency, dedicated to solving agricultural problems from field to table, sustaining agroecosystems, and ensuring the economic competitiveness of U.S. agriculture. Through the SCINet/Artificial Intelligence (AI) Center of Excellence Research Participation Program, fellows work on agriculture‑related problems spanning genomes to continents and sub‑daily to evolutionary timescales, applying AI, machine learning, and high‑performance computing (HPC) to integrate large, diverse datasets and foster cross‑disciplinary, cross‑location collaboration.

Responsibilities

Under mentor guidance, you will investigate interactions between maize and agriculturally important fungal pathogens (initially Fusarium and Aspergillus species) that cause crop diseases and mycotoxin contamination. The research will integrate genome sequences, genetic variation, transcriptomic profiles, functional annotations, protein structures, molecular networks, and disease‑resistance phenotypes. You will apply AI, machine learning, and bioinformatics to connect genotypes with phenotypes, identify maize and fungal genes/alleles influencing disease resistance, pathogen virulence, toxin production, and host recognition. You will also develop and apply foundation models for fungal DNA and protein sequences, adapt long‑context DNA language models to fungal genomes for genome‑wide variant effect prediction, and use protein language models and structural methods to predict maize–fungal protein interactions and inform disease‑ and mycotoxin‑control strategies.

Requirements

U.S. citizenship only. Candidates must be currently pursuing or have received a doctoral degree in a relevant field.

Qualifications

Previous experience with both maize and fungal pathogens is not required. Strong computational backgrounds and an interest in applying artificial intelligence to agricultural genomics are encouraged.

Skills

• Experience analyzing genomic, genetic, transcriptomic, or other biological datasets
• Experience with sequence analysis, variant analysis, comparative genomics, or related computational genomics approaches
• Experience analyzing RNA‑sequencing data
• Experience using Linux/Unix and high‑performance computing systems
• Experience with Python, R, or other scientific programming languages
• Experience with AI/ML approaches
• Familiarity with