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ARS SCINet and AI Center of Excellence Postdoctoral Fellows Program (FY23)


The SCINet Program, in collaboration with the ARS Artificial Intelligence Center of Excellence (AI-COE), is calling for proposals for funding to support postdoctoral fellows to be mentored by ARS scientists. The goal of the fellowship program is to develop the next generation of ARS scientists with expertise in conducting and leading individual and collaborative research using computationally intensive approaches.

Each fellow should: (1) be involved in individual and collaborative, multi-unit research, (2) have training and leadership opportunities, and (3) contribute to the overall success of SCNet or the AI-COE and the Fellows Program. In addition, each fellow will have the opportunity to take advantage of training courses that build computational literacy, such as in data science, AI, bioinformatics, and geospatial analyses on SCINet’s high-performance computing clusters (Ceres, Atlas).

The call for proposals is now open. The deadline for proposal submission is Friday, December 23, 2022. Please visit for detailed instructions. Please note that FY22 awardees (PIs) will not be eligible for an FY23 award.

Proposals Funded in 2022 Mentor Co-mentor(s)
Determining the structure and function of proteins of foodborne and plant pathogens using Alphafold2 and top-down proteomic analysis Clifton K. Fagerquist
AI-driven phenotype extraction from UAS imagery for crop genetics and breeding Jacob Washburn Alisa Coffin, Max Feldman
Machine learning approaches to gain structural and functional insights of genes regulating climate adaptability Carson Andorf Taner Sen
Inferring Protein Function for Enhanced Breeding using Machine Learning and Protein Structure Prediction Taner Sen Carson Andorf
A MATLAB toolbox for dynamic prediction of meteorological drought across Southern Great Plains Daniel Moriasi Ali Mirchi
Optimization of AI-based microscope image analysis with the Blackbird imaging robot Lance Cadle-Davidson Yu Jiang
Automated Detection of Insect Species in Food and Processing Environments using Artificial Intelligence Paul R Armstrong Lester Pordesimo
Improving the accuracy and scope of machine learning tools for camera trap-based ecological data analysis across US grazinglands Hailey Wilmer
A broadly deployable deep learning workflow for image feature identification, segmentation, and data extraction. Devin A Rippner Jeff Neyhart , Kayla Altendorf, Garett Heineck, Andrew McElrone
An AI approach for discovering novel blast disease resistance sources in rice. Jeremy Edwards Yulin Jia
Predicting interspecies transmission of influenza A virus from swine to humans with machine learning Tavis Anderson Amy Vincent
Modeling the Spread and Adaptation of Stored-Product Insect Pests in the Face of Climate Change Alison Gerken Rob Morrison
Using AI to Analyze Climate Effects on Crop Performance Xianran Li
AI/ML and Deep Learning to Enhance Understanding of Dietary Patterns that Promote Human Health David Baer Lauren O’Connor
The evolutionary genomics of Macrophomina phaseolina. Peter Montgomery Henry
Spatial multi-criterion optimization of agricultural ecosystem services at the landscape scale Sarah Goslee
Application of machine learning in livestock genomics George Liu
Determining the pervasiveness of hybridization and introgression in agriculture and the driving mechanisms Christopher Owen
Do you know someone who might be interested in becoming an ORISE Fellow with SCINet or the AI Center of Excellence? Contact the mentor listed above. To see all open opportunities, visit: