USDA-ARS SCINet/AI-COE Postdoctoral Fellowship in Mitigation of Antimicrobial Resistance in Food-borne Pathogens Associated with Chicken Production

*Applications will be reviewed on a rolling-basis and this posting could close before the deadline.

ARS Office/Lab and Location: A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), within the U.S. National Poultry Research Center, located in Athens, Georgia.  

The Agricultural Research Service (ARS) is the U.S. Department of Agriculture's chief scientific in-house research agency with a mission to find solutions to agricultural problems that affect Americans every day from field to table. ARS will deliver cutting-edge, scientific tools and innovative solutions for American farmers, producers, industry, and communities to support the nourishment and well-being of all people; sustain our nation’s agroecosystems and natural resources; and ensure the economic competitiveness and excellence of our agriculture. The vision of the agency is to provide global leadership in agricultural discoveries through scientific excellence.

Research Project: 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 postdoctoral fellows 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 solving agriculture-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 (HPC) clusters. 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 requiring the HPC environment. Training will be provided in data science, scientific computing, AI/machine learning, and related topics as needed for the fellow to complete their research.

The ORISE SCINet fellow will participate and co-lead research under the guidance of a mentor that aims to develop and validate a machine learning tool utilizing longitudinal multiomics data (genomics and metagenomics) to predict chicken host factors, management practices, and environmental parameters that are correlated with a reduction in antimicrobial resistance development in Salmonella.

Learning Objectives: The fellow will be trained in statistical machine learning including, but not limited to, Bayesian Network and Dynamic Bayesian Network approaches. The fellow will participate in USDA-ARS SCINet training workshops and will attend professional conferences, meetings, and workshops to further their skills and to facilitate networking and professional development. In addition, the fellow will participate with the Salmonella grand challenge team and the SCINet Microbiome working group.

USDA-ARS Contact: If you have questions about the nature of the research, please contact Dr. Adelumola Oladeinde, US National Poultry Research Center, Athens, Georgia, ade.oladeinde@usda.gov.

Anticipated Appointment Start Date: 2023; Start date is flexible and will depend on a variety of factors.

Appointment Length: The appointment will initially be for one year but may be renewed upon recommendation of the mentor and ARS.

Level of Participation: The appointment is full-time.

Participant Stipend: The participant(s) will receive a monthly stipend commensurate with educational level and experience. The current stipend range for this opportunity is $85,000 - $95,000/year plus a supplement to offset a health insurance premium. 

Citizenship Requirements: This opportunity is available to U.S. citizens, Lawful Permanent Residents (LPR), and foreign nationals. Non-U.S. citizen applicants should refer to the Guidelines for Non-U.S. Citizens Details page of the program website for information about the valid immigration statuses that are acceptable for program participation.

ORISE Information: This program, administered by ORAU through its contract with the U.S. Department of Energy (DOE) to manage the Oak Ridge Institute for Science and Education (ORISE), was established through an interagency agreement between DOE and ARS. Participants do not become employees of USDA, ARS, DOE or the program administrator, and there are no employment-related benefits. Proof of health insurance is required for participation in this program. Health insurance can be obtained through ORISE.

Questions: Please visit our Program Website. After reading, if you have additional questions about the application process, please email ORISE.ARS.SCINet@orau.org and include the reference code for this opportunity.

Qualifications

The qualified candidate should have received a doctoral degree in one of the relevant fields, or be currently pursuing the degree to be received before the appointment start date.

Preferred skills:

• Background and experience in computer science and statistics
• Experience developing, testing, and refining machine learning models
• Experience developing HPC workflows
• Excellent written and oral communication skills
• Experience in team and collaborative scientific environments