Postdoctoral Research Associate (Research Geneticist)
USDA, Agricultural Research Service
Application
Details
Posted: 10-Jan-23
Location: Chatsworth, New Jersey
Type: Full Time
Salary: 75,833
Categories:
Biology
Botany
Years of Experience:
2 - 4
Salary Details:
Salary represents locality pay at grade GS-11, step 1.
The incumbent in the position is eligible for benefits including the Federal Employee Retirement System (FERS), Federal Employee Group Life Insurance program (FEGLI), Federal Employee Health Benefits (FEHB) program, and Thrift Savings Plan (TSP).
Required Education:
Doctorate
The Neyhart Lab in the Genetic Improvement for Fruits and Vegetables Laboratory seeks an energetic and motivated postdoctoral research associate to join a growing team and investigate predictive breeding in cranberry. The incumbent will lead research related to the use of high-throughput phenotyping and genomewide prediction in cranberry, but will have wide latitude to explore other research projects related to cranberry or blueberry pre-breeding and genetics. The incumbent will be expected to develop research questions, collect data, analyze results, present at local and national scientific meetings, and publish peer-reviewed papers. This position will be based at the Rutgers University P.E. Marucci Center for Blueberry and Cranberry Research and Extension in Chatsworth, New Jersey, the premier Vaccinium research center in the United States. Opportunities will be plentiful for collaborating with other USDA-ARS and university scientists locally and nationally, as well as interacting with grower and processor stakeholders.
USDA-ARS is an equal opportunity employer and provider.
Requirements:
Regulations require that only United States citizens and permanent residents be considered for this position
A Ph.D. in plant breeding, genetics, agronomy, horticulture, plant pathology, or a related field must have been awarded no more than 4 years prior to starting this position
Experience applying genomic data in a breeding program or conducting genetic studies in annual or perennial crops
Excellent organizational, interpersonal interaction, and communication skills
Experience in genomewide prediction, high-throughput phenotyping, or analyzing other high-dimension data is preferred
Fluency in R, Python, or another analytic computing language is preferred