*Applications are reviewed on a rolling-basis and this posting could close before the deadline.
ARS Office/Lab and Location: Multiple master’s level research opportunities are currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS) located in Beltsville, Maryland.
This research opportunity is part of the SCINet Fellowship program at ARS. All fellows will spend time at headquarters for some of their training, but will be based in Beltsville, Maryland for more specific training. The SCINet/Big Data Program at ARS offers research opportunities to motivated participants interested in solving 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, as well as metadata enhancement for data discovery. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (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, linkage, and analysis of large, diverse datasets that benefit from high performance computers (HPC). The objective of these opportunities is to facilitate cross-disciplinary, cross-location research through collaborative research on problems of interest to each participant 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.
Research Project: Agro-ecosystem dynamics at large spatial extents cannot be easily predicted by simply extrapolating from local, on-farm estimates. Agricultural yield, snow melt, and nitrogen or sediment in rivers are some examples where on-farm estimates need to be expanded from regional to continental scales through the integration of large data streams at fine-scale temporal resolutions. Research areas also include large scale data analyses for human nutrition, food safety and quality, animal and crop production and protection. Data analyses and linkage via enhanced metadata for the semantic web using AI (Natural Language Processing (NLP) and machine learning, etc.) are key components of the Agricultural Research AI enterprise. Research activities have been mainly conducted in local computing environments which constrains the applications in terms of computing capability and the size of the study area. High performance computing (HPC) or cloud computing are needed to enhance the scientific computing and data management capability.
Learning Objectives: The selected participant will have the opportunity to learn a range of computational skills needed to conduct research analyses in the above areas in an HPC or cloud-based environment. Under the guidance of a mentor, the participant will learn how to develop and co-lead ARS-wide workshops to synthesize and integrate climate and environmental data with land use data, and will help organize the scientific community engaged in this topic. 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 complex agro-ecosystem datasets at regional to continental scales.
Mentor(s): The mentor for this opportunity is Jennifer Woodward-Greene (Jennifer.firstname.lastname@example.org). If you have questions about the nature of the research please contact the mentor(s).
Anticipated Appointment Start Date: After May 1, 2021. 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.