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U.S. Department of Agriculture
3 days ago
USDA-ARS SCINet/AI-COE Postdoctoral Fellowship in AI-Driven Maize-Fungal Pathogen Genomics U.S. Department of Agriculture (USDA) in United States
Degree Level
Postdoc
Field of study
Computer Science
Funding
Monthly stipend commensurate with educational level and experience; current range $100,000-$110,000/year; supplement to offset health insurance costs; additional funds available for supplies, travel, and relocation costs if applicable.
Deadline
Dec 4, 2026
Country
United States
University
U.S. Department of Agriculture
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About this position
This postdoctoral fellowship is hosted by the U.S. Department of Agriculture’s Agricultural Research Service (ARS) within its SCINet/Artificial Intelligence Center of Excellence research participation program. The work is based across the Corn Insects and Crop Genetics Research Unit in Ames, Iowa, and the National Center for Agricultural Utilization Research in Peoria, Illinois.
The project focuses on AI-driven maize–fungal pathogen genomics, with initial emphasis on Fusarium and Aspergillus species. These agriculturally important fungi are studied because they affect crop disease, mycotoxin contamination, grain quality, and food and feed safety. The fellow will work under mentor guidance on integrating genome sequences, genetic variation, transcriptomic profiles, functional annotations, protein structures, molecular networks, and disease-resistance phenotypes to connect genotypes with phenotypes.
The fellowship offers the opportunity to contribute to AI and machine learning approaches in agricultural genomics, including foundation models for fungal DNA and protein sequences. The research will explore long-context DNA language models adapted to fungal genomes, genome-wide prediction of variant effects across pathogen populations, and protein language models and structural methods for analyzing protein variation and predicting maize–fungal protein interactions. The overall aim is to help identify genes, alleles, variants, and genomic loci linked to disease resistance, pathogen virulence, toxin production, and host recognition.
The training environment is strongly computational and interdisciplinary. The fellow will gain experience in host–pathogen genomics, multi-omic data integration, RNA-seq analysis, comparative and population genomics, pangenome relationships, and the development of reproducible GPU- and CPU-based high-performance computing pipelines. The role also emphasizes packaging analytical methods as reusable research software and community resources, and participation in SCINet and AI Center of Excellence meetings, seminars, and professional-development activities.
Additional opportunities may include collaboration with external AI knowledge-holders, scientific groups, students, and researchers working on computational genomics and AI methods in agriculture.
Funding details
Monthly stipend commensurate with educational level and experience; current range $100,000-$110,000/year; supplement to offset health insurance costs; additional funds available for supplies, travel, and relocation costs if applicable.
What's required
The candidate should be currently pursuing or have received a doctoral degree in a relevant field. Previous experience with both maize and fungal pathogens is not required. Candidates should have a strong computational background and interest in applying artificial intelligence to agricultural genomics. Preferred experience includes analyzing genomic, genetic, transcriptomic, or other biological datasets; sequence analysis, variant analysis, comparative genomics, or related computational genomics approaches; RNA-sequencing analysis; Linux/Unix and high-performance computing systems; Python, R, or another scientific programming language; AI/ML approaches; tools such as BLAST, MMseqs, and multiple sequence alignments; knowledge of plant genetics, fungal biology, plant pathology, or related areas; and protein-structure prediction, protein-protein interaction analysis, or structural bioinformatics. U.S. citizenship is required.
How to apply
Scroll to the bottom of the opportunity page and click APPLY. Submit an application, transcripts, a current resume/CV, and two educational or professional recommendations. All documents must be in English or include an official English translation.
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