*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 and located at various facilities with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS).
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 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.
Predicting the nation’s food supply and ensuring its safety while preserving the natural resources as the environmental drivers continue to change is challenging, and requires integrating and computing vast amounts of high quality data of many different types. Data on U.S. agriculture have been collected since the 1860s. Data streams now include ground-, UAV-, air-, and satellite-based sensors and imagery as well as analyses using genomic data and manually collected data, including animal behavior studies. The advanced computing technologies, such as high performance computing, cloud computing, and AI technologies, needed to fully take advantage of these data are still being developed.
Learning Objectives: The participant will have the opportunity to learn about the challenges in predicting dynamics of agro-ecosystems while learning a range of computational skills needed to conduct these analyses. The participant will also learn AI technologies relevant to these problems, and will develop and co-lead ARS-wide workshops resulting in a community of scientific practice with AI. The participant will also have the opportunity to collaborate with multiple USDA ARS scientists on data analysis projects, and to write collaborative scientific papers dealing with AI across multiple spatial and temporal scales. This unique fellowship will allow participants the opportunities to apply their programming skills to real-world problems.
USDA-ARS Contact: If you have questions about the nature of the research please contact Debra Peters (email@example.com).
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.
- Experience modeling spatially resolved and/or time series data
- Experience working with large, diverse datasets and data mining approaches
- Proficiency in R or Python
- Strong computational skills
- Strong database skills
- Strong oral and written communication skills