Job Details
Research Project: Under the guidance of the mentor, the candidate will utilize a functional genomics and advanced computational approach to examine (epi)genetic factors responsible for reproductive success in fish species, especially channel and blue catfish. As epigenetic marks have been linked to reproduction, and epigenetic remodeling of gametes can occur due to physiological and environmental changes, the successful candidate will characterize the epigenetic landscape of catfish under various culture conditions. The participant will then use descriptive and predictive data mining (machine learning) approaches to define the genetic associations with reproductive performance indices and stratify fish broodstocks into performance groups to maximize reproductive success toward our goal of precision aquaculture.
Additionally, a major research focus of the AAHRU includes the development of tools that will enhance fish and shellfish resistance to infectious bacteria, viruses, and parasites. Therefore, a highly productive candidate will have the opportunity to learn and integrate their skills into other multi-omics research focused on disease states, including modeling host-pathogen interaction dynamics, determining pathogen and host factors that influence virulence and/or disease resistance, validating candidate loci responsible for quantitative and qualitative traits, and/or determining gene/protein functions and causal regulatory elements relevant to fish and shellfish immunology.valid immigration statuses that are acceptable for program participation.
Requirements
The qualified candidate should have received a doctoral degree in one of the relevant fields.
Preferred skills:
- Strong research focus in bioinformatics, computational biology, or data sciences
- Proficiency in Linux, R, Perl, and/or Python
- Experience in the analyses of multi-omics datasets
- Experience developing, testing, and refining machine learning models
- Experience developing HPC workflows
- Excellent written and oral communication skills
- Evidence of research productivity through a relevant publication record
Contact Information
U.S. Department of Agriculture (USDA)https://www.zintellect.com/Opportunity/Details/USDA-ARS-SCINet-2024-0285
https://www.zintellect.com/Account/ApplicantRegister/24987
Post Date: 8/9/2024 2:17:13 PM
Closing Date: 10/11/2024 12:00:00 AM
