USDA-ARS SCINet Postdoctoral Fellowship in Machine Learning, Protein Structure, and Functional Annotations
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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. All transcripts must be in English or include an official English translation. 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.
*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), Crop Improvement and Genetics Research Unit, located in Albany, California.
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.
This research opportunity will be part of the GrainGenes (https://wheat.pw.usda.gov) ARS research project located in the beautiful San Francisco Bay Area. Under the guidance of a mentor and in collaboration with scientists and support staff, the participant will have the opportunity to gain experience and learn about the challenges of using hybrid machine learning approaches and cutting-edge protein tertiary structure predictions to develop generalized methods to enhance the functional annotation space of multiple species and computational pipelines with the aim of supporting breeding efforts that can help improve agronomically-important traits.
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 applications of machine learning methods to understand protein structure, function, and dynamics. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on machine learning, artificial intelligence, protein structure prediction to take the lead in creating open-source, reusable computational pipelines and writing collaborative scientific papers.
USDA-ARS Contact: If you have questions about the nature of the research, please contact Taner Sen (taner.sen@usda.gov).
Anticipated Appointment Start Date: June 2022. 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. The current stipend for this opportunity is $7,565/month. A $545.60/month health insurance supplement will be provided along with a $9,700/year travel and miscellaneous allowance.
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. If you have additional questions about the application process please email ORISE.ARS.SCINet@orau.org and include the reference code for this opportunity. [this code will be added later]
The qualified candidate should be currently pursuing or have received a doctoral degree in one of the relevant fields.
Preferred skills:
- Experience in computer science, bioinformatics or computational biology
- Experience in machine learning and artificial intelligence
- Experience with working with genetics and genomic data
- Experience working 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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