USDA-ARS SCINet Postdoctoral Fellowship in Using AI to Analyze Climate Effects on Crop Performance: Washington

Agency
U.S. Department of Agriculture (USDA)
Location
Pullman, Washington
Job Category
Post Doctoral Appointments
Salary
Competitive Monthly Stipend
Last Date to Apply
09/30/2022
Website
https://www.zintellect.com/Opportunity/Details/USDA-ARS-2022-0139
Description
*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), Wheat Health, Genetics, and Quality Research Unit, Pullman, WA. 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 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. The participant will research the impact of changing climatic conditions to crop performance trends through integrating state-of-the-art analytics and multi-dimensional big genomic and environmental data by harnessing the computational power of USDA ARS SCINet HPC. The overarching goal is to explain the major trends in crop production at the region-, state-, and nation-wide level using biologically meaningful information, and to develop predictive models to forecast trends in crop performance. Under the guidance of the ARS mentor, the fellow will co-lead a new AI Center of Excellence working group using AI to analyze climate effects on crop performance. Learning Objectives: The participant will learn HPC computing technologies and will help develop and co-lead ARS-wide workshops, resulting in a community of scientific practice to understand and explore the impact of climate change on crop performance. The participant will have the opportunity to collaborate with multiple USDA-ARS scientists on ingrained climate change projects, and to write collaborative scientific papers about using publicly accessible datasets to empirically evaluate climate change. USDA-ARS Contact: If you have questions about the nature of the research, please contact Xianran Li (xianran.li@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 the mentor and 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 only. 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. If you have additional questions about the application process, please email USDA-ARS@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 listed below. Preferred skills: Proficiency in Linux and R. Experience in Python or Perl Experience in big data analysis Strong oral and written communication skills Ability to work collaboratively with researchers from different disciplines
Contact Person
USDA-ARS@orau.org
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