This job has Expired
USDA-ARS SCINet Fellowship on Using Museum Collections to Develop a Machine Learning Application to Stop Biological Invasions: Washington, D.C.
Job Description
- Agency
- U.S. Department of Agriculture (USDA)
- Location
- Washington, D.C.
- Job Category
- Internships
- Salary
- Monthly Stipend TBD
- Last Date to Apply
- 08/04/2023
- Website
- https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0266
- Description
- *Applications are reviewed on a rolling basis. ARS Office/Lab and Location: A research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), located in Washington, D.C. 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 support staff conduct research within the ARS research enterprise. The SCINet AI Center of Excellence Innovation fund of the USDA ARS offers research opportunities to motivated graduate and postdoctoral fellows interested in conducting research 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. Under the guidance of a mentor, the participant will have the opportunity to gain experience in and learn about the challenges of arthropod biological invasions in agroecosystems, while learning a range of computational skills needed to develop agricultural machine learning applications and perform scientific studies, including museum science, study design, machine learning tools in Python and R, and data visualization. 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 on biological invasions. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on machine learning projects in agroecosystems and write collaborative peer-reviewed manuscripts on using arthropod museum specimens to develop machine learning applications in order to prevent arthropod biological invasions (especially beetles). Mentor(s): The mentor(s) for this opportunity is Christopher Owen (christopher.owen@usda.gov). Please contact the mentor if you have questions about this opportunity. 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 ARS and is contingent on the availability of funds. Level of Participation: The appointment is full-time. Participant Stipend: The participant 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. 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 master's or doctoral degree in one of the relevant fields or be currently pursuing one of the degrees with completion prior to start of appointment. Degree must have been received within the past six months. Preferred Skills: Experience with machine learning applications and studies Experience with museum specimens Proficiency in HPC, linux, and Python or R Strong oral and written communication skills
- Contact Person
- ORISE.ARS.SCINet@orau.org
Bookmark the permalink .
*Please mention you saw this ad on AcademicJobs.*
