USDA-ARS SCINet/AI-COE Postdoctoral Fellowship in Machine Learning Reveals RNAi Molecular Targets From Transcriptomic Architectures of Mycotoxigenic Fungi: Minnesota

Agency

U.S. Department of Agriculture (USDA)

Location

St. Paul, Minnesota

Job Category

Post Doctoral Appointments

Salary

Monthly Stipend TBD

Last Date to Apply

12/31/2023

Website

https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2023-0269

Description

*Applications are reviewed on a rolling basis. ARS Office/Lab and Location: A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Cereal Disease Laboratory in Saint Paul, Minnesota. Teleworking is an option. 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 postdoctoral fellows conduct research within the ARS research enterprise. Research Project: The SCINet/Big Data Research Participation Program of the USDA ARS offers research opportunities to motivated postdoctoral fellows interested in solving agriculture-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 (HPC) clusters. 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 requiring the HPC environment. Training will be provided in data science, scientific computing, AI/machine learning, and related topics as needed for the fellow to complete their research. Throughout the course of this research project, the participant will develop expertise in the implementation of machine learning algorithms to generate gene regulatory networks (GRNs) to help identify disease-control targets for fungal pathogens of crops. Fungal plant pathogens cause billion-dollar losses annually in the United States and threaten human and animal health with toxins (mycotoxins). Chemical fungicides have been the main line of defense against these organisms. However, the sustainability of this approach is being challenged by an increased prevalence of fungicide resistance and by the evolution of cross-resistance from agricultural fields to clinical settings. The fellow will use publicly available big transcriptomic data to identify genes regulating fungal virulence, growth, and toxin production to better understand the evolution of fungal pathogenesis and to select candidate targets for RNAi-based control of Fusarium head blight. Additionally, a successful candidate will develop bioinformatic pipelines that facilitate that harnessing of similar datasets for efforts to develop sustainable RNAi targets in other systems. Learning Objectives: The participant will learn about a wide range of activities related to plant pathology, the evolution of fungal pathogens, machine learning and multi -omic techniques. Additionally, the fellow will have opportunities for mentorship and science communication activities in support of the BDI and the ARS AI Center of Excellence (AI COE). The participant will also have the opportunity to take online courses in scientific topics, such as R, Python and statistics, and to learn collaboration and leadership skills through workshop and collaborative group experience. Mentor(s): The mentor(s) for this opportunity is Milton Drott (milton.drott@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 two years 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 current stipend range for this opportunity is $85,000 - $95,000/year plus a supplement to offset a health insurance premium. 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 ORISE.ARS.SCINet@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 or be currently pursuing the degree with completion before December 31, 2023. Degree must have been received within the past five years. Preferred Skills: Experience developing, testing, and refining machine learning models Experience developing HPC workflows Experience with comparative and pan genomics Excellent written and oral communication skills Experience in team and collaborative scientific environments

Contact Person

ORISE.ARS.SCINet@orau.org

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