Organization U.S. Department of Agriculture (USDA) Reference Code USDA-ARS-SCINet- Application Deadline 7/17/2026 8:38:38 AM Eastern Time Zone Program Overview The U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS) is offering a postdoctoral fellowship situated in Columbia, Missouri. The mission of ARS involves problem‑solving research across food and agricultural areas, including plant production and protection, animal production and protection, natural resources, sustainable agricultural systems, nutrition, food safety, and quality. The ARS research enterprise operates in 46 states, Puerto Rico, and the U.S. Virgin Islands. Major investments in computing, networking, and storage infrastructure support the research goals. Training in data and information management is integral to the integrity, security, and accessibility of research findings. Research Project The SCINet/Big Data Research Participation Program provides research opportunities to postdoctoral fellows for solving agriculture‑related problems from the genome to the continent. One goal is to develop and apply new technologies—including AI and machine learning—to address complex agricultural challenges that benefit from collaboration across scientific disciplines and geographic locations. The fellowship facilitates cross‑disciplinary research through collaboration on problems of interest to each applicant, leveraging high‑performance computing (HPC) environments. Fellows will receive training in data science, scientific computing, AI/machine learning, and related topics as needed. This project involves a team of USDA‑ARS scientists: Paula Chen (PGRU), Lianna Walker (PGRU), Jacob Washburn (PGRU), Norman Best (PGRU), Bethany Redel (PGRU), Dave Kang (BICRL), and Adam Rivers (GBRU). Expertise ranges from machine learning and artificial intelligence applications in agriculture to gene editing of livestock and crops. The focus is on developing a cross‑kingdom genome editing toolkit, especially for CRISPR/Cas9, to predict guide‑RNA efficiency and specificity prior to in‑vivo validation. The toolkit will initially target the chicken genome, with subsequent expansions to pigs, maize, microbes, and other species. Additionally, a single‑cell atlas for turkey immune organs will be developed for downstream gene editing applications. Learning Objectives Fellows will learn gene editing techniques, genomic selection, and AI/ML methods for data manipulation and prediction. They will develop and troubleshoot AI/ML tools, interpret outcomes, and present findings at conferences such as the Plant and Animal Genome Conference (PAG). Workshops in genome editing, single‑cell RNA sequencing, AI, and ML are available online and on the University of Missouri campus. Mentor(s) Paula Chen ( ). Anticipated Appointment Start Date 2026. Start date is flexible and will depend on a variety of factors. Appointment Length The appointment is initially for one year and may be renewed upon recommendation of ARS and depending on the availability of funds. Level of Participation Full‑time. Participant Stipend The stipend range is $85,000 – $95,000 per year, plus a supplement to offset a health‑insurance premium. Stipend aligns with the fellow’s educational level and experience. Citizenship Requirements This opportunity is available to U.S. citizens only. ORISE Information Participants are managed by ORAU under a contract with the Department of Energy to administer the Oak Ridge Institute for Science and Education (ORISE). Participants do not become employees of USDA, ARS, DOE, or ORAU, and they receive no employment‑related benefits. Proof of health insurance is required; insured through ORISE. Participants must comply with Environmental, Safety, and Health (ES&H) requirements, including COVID‑19 protocols. Qualifications Applicants should be currently pursuing or have received a doctoral degree in one of the relevant fields. Eligibility Requirements Citizenship: U.S. Citizen Only Degree: Doctoral Degree Discipline(s): Computer, Information, and Data Sciences (including Artificial Intelligence, Robotics, Computer Vision, Human Language Processing, and Machine Learning) Computer Science (general) Life Health and Medical Sciences Mathematics and Statistics Application Requirements Application form Unofficial transcript or copy of academic records Current résumé/CV Two educational or professional recommendations Copies of an abstract or reprint of an article All documents in English or with official English translation Notes Applications are reviewed on a rolling basis. For additional information, please visit the program website or contact with the reference code. #J-18808-Ljbffr