ARS Office/Lab and Location: A postdoctoral research opportunity is available with the U.S. Department of Agriculture (USDA), Agricultural Research Service (ARS), Animal Genomics and Improvement Laboratory, Beltsville, Maryland.
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.
Under the guidance of a mentor, the participant will have the opportunity to gain experience in and learn about the applications of multi-layer ‘omics data, including issues related to quality control, processing, and use, while learning a range of computational skills needed to conduct complex bioinformatics analyses. Specifically, the participant will develop a machine-learning pipeline of copy number variation detection for SCINet Resources and study gene networks underlying complex traits using SNP perturbation in the field of genome annotation.
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 livestock genomics research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on genome assembly, genomic prediction, genome-wide association study (GWAS), genotype imputation, phenotype quality check, and others.
Anticipated Appointment Start Date: August 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.
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.
The qualified candidate should have received a doctoral degree in one of the relevant fields (e.g. Computer Science, Computational Biology, Bioinformatics, Genetics).
- Proficiency in Linux and computational languages like R and Fortran, C/C++, or Python
- Strong oral and written communication skills
- Knowledge of genetics, next-generation sequencing, and genome annotation and assembly
- Preference will be given to candidates with a strong publication record and evidence of substantial research productivity