USDA-ARS Fellowship in Data Management and Machine Learning
To submit your application, scroll to the bottom of this opportunity and click APPLY.
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. 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 are reviewed on a rolling-basis.
ARS Office/Lab and Location: A research opportunity is currently available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), located in Albany, California.
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: GrainGenes (https://wheat.pw.usda.gov) is a digital platform that serves small grains research communities as their central data repository and as a facilitator for community activities. It is funded by the U.S. Department of Agriculture-Agricultural Research Service to ensure long-term data sustainability and interactivity, primarily for wheat, barley, oat, and rye. As part of a highly collaborative, multi-disciplinary team, the selected candidate will use his/her computational biology and machine learning background to help develop tools to transform data query, access, display, and sharing, perform analyses, present results, and publish manuscripts.
Learning Objectives: The participant will learn about the most recent advances in machine learning and data management in agricultural research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on using machine learning approaches, develop an expertise in accessing and managing agricultural data, learn how to present the outcomes of the research, and how to write scientific papers applying these approaches.
Mentor(s): The mentor for this opportunity is Taner Sen (taner.sen@usda.gov). If you have questions about the nature of the research, please contact the mentor(s).
Anticipated Appointment Start Date: 2025. 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. The anticipated stipend is $90,000 annually.
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.PacificWest@orau.org and include the reference code for this opportunity.
The qualified candidate should be currently pursuing or have received a doctoral degree in the one of the relevant fields.
Preferred skills:
- Experience in computer science, bioinformatics, or computational biology
- Experience in machine learning and artificial intelligence
- Experience with genetics and genomic data
- Experience with large, diverse datasets and data mining approaches
- Proficiency in Linux and computational programming
- Strong computational and analytical skills
- Strong oral and written communication skills
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