USDA-ARS SCINet Computer Vision and A.I. for Insect Identification Postdoctoral Fellowship

The U.S. Department of Agriculture - Agricultural Research Service (USDA ARS) mission involves problem-solving research in the widely diverse food and agricultural areas encompassing plant production and protection; animal production and protection; natural resources and sustainable agricultural systems; and nutrition; food safety; and quality. The programs are conducted in 46 of the 50 States, Puerto Rico, and the U.S. Virgin Islands. For ARS to maintain its standing as a premier scientific organization, major investments in computing, networking, and storage infrastructure are required. Training in data and information management are integral to the integrity, security, and accessibility of research findings, results, and outcomes within the ARS research enterprise. Nearly 2000 scientists and support staff conduct research within the ARS research enterprise.

The SCINet/Big Data Research Participation Program of the USDA ARS offers research opportunities to motivated postdoctoral fellows interested in collaborating on agricultural-related problems at a range of spatial and temporal scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning, to help solve complex agricultural problems that also depend on collaboration across scientific disciplines and geographic locations. In addition, many of these technologies rely on the synthesis, integration, and analysis of large, diverse datasets that benefit from high performance computing clusters (HPC). The objective of this fellowship is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each applicant and amenable to or required by the HPC environment. Training will be provided in specific AI, machine learning, deep learning, and statistical software needed for a fellow to use the HPC to analyze large datasets.

Insect infestations in food storage and processing facilities can be destructive and highly disruptive to production. The participant, under the guidance of the mentors, will study and use artificial intelligence (AI) and machine learning (ML) methods for automated insect identification from images in a broad range of food storage environments. It is envisioned this will help lead to the development of practical, remote and autonomous monitoring devices used to detect insect activity, identify species and infestations in food storage facilities and in the products they house. Ultimately this will lead to better response and control times over the manual monitoring methods currently used. 

The participant will learn HPC computing technologies and will help develop and co-lead ARS-wide workshops, resulting in a community of scientific practice for a focus group using AI and ML in the image processing arena and other areas using AI. The participant will have the opportunity to collaborate with multiple USDA ARS scientists in the development of feature extraction methods from images over a broad range of topics and report these findings.

Use the link below to submit an application or contact the sponsor.

https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0152