USDA-ARS Machine Learning & AI in Nutrition Research Fellowship
A complete application consists of:
- An application
- Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal institution systems may be submitted. All transcripts must be in English or include an official English translation. Click here for detailed information about acceptable transcripts.
- A current resume/CV, including academic history, employment history, relevant experiences, and publication list
- Two educational or professional recommendations
All documents must be in English or include an official English translation.
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*Applications may be reviewed on a rolling-basis and this posting could close before the deadline.
ARS Office/Lab and Location: Multiple postdoctoral research opportunities are available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Western Human Nutrition Research Center located in Davis, California.
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 2,000 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 working on agricultural- and natural resource-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 computers (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 the HPC.
A major goal of the ARS Human Nutrition Program is to provide dietary guidance to Americans to improve health. As modern research now includes large-scale data, new methods are needed to (1) determine which features of dietary intake are most important for improved health and (2) predict health outcomes for proposed dietary guidance. Machine learning algorithms are designed to “learn” from data to predict future outcomes. However, advances in high performance computing, machine learning and AI are yet to be fully deployed in the domain of nutrition research.
Learning Objectives: The participant will have the opportunity to learn about the challenges in predicting the effect of diet on health outcomes while learning a range of computational skills needed to conduct these analyses. The types of data may include dietary intake, physiology, physical activity, stool and plasma markers of inflammation, gut microbiome, genotype, and/or stress. The participant will learn the machine learning approaches to predict the effects of diet, will develop standardized machine learning workflows for nutrition research and will develop and co-lead ARS-wide workshops resulting in a community of scientific practice applying machine learning to nutrition data. The participant will also have the opportunity to collaborate with USDA ARS scientists on data analysis projects, and to write collaborative scientific papers using machine learning using data from studies in nutrition.
USDA-ARS Contact: If you have questions about the nature of the research please contact Debra Peters (deb.peters@usda.gov).
Anticipated Appointment Start Date: 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 ARS and is contingent on the availability of funds.
Level of Participation: The appointment is full-time.
Participant Stipend: The participant(s) will receive a monthly stipend commensurate with educational level and experience.
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 USDA-ARS@orau.org and include the reference code for this opportunity.
The qualified candidate should have received a doctoral degree in one of the relevant fields.
Preferred skills:
- Experience working with large, diverse datasets and data mining approaches
- Experience implementing software on Unix and HPC using SLURM submission scheduling
- Experience with Git/Github
- Proficiency in R and Python
- Strong computational skills
- Strong oral and written communication skills
- Skill in the facilitation of meetings and in working with people
- Experience working with dietary and human data is a plus
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