ARS SCINet and AI Center of Excellence Postdoctoral Fellows Program (FY23)
Overview
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 https://forms.office.com/g/ULtMeH9Kp9 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 |