Postdoctoral Fellowship in Machine Learning, Protein Structure, and Functional Annotations

U.S. Department of Agriculture
Agricultural Research Service
United StatesCaliforniaAlbany


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 ( 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.

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.


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

Start date

As soon as possible

How to Apply

Apply at


Taner Sen