Job no: 533916
Work type: Research Faculty
Senior management: Agriculture & Life Sciences
Department: School of Plant & Environmental Sci
Location: Blacksburg, Virginia
Categories: Agriculture / Life Science, Research / Scientific

JOB DESCRIPTION

The School of Plant and Environmental Science at Virginia Tech is seeking a highly motivated research associate to help lead the barley breeding efforts within the small grains breeding program at Virginia Tech under the mentorship of the program lead, Principal Investigator (PI). The applicant is expected to develop and apply novel predictive breeding methods using high-dimensional genomic information and high-throughput phenotyping. The successful applicant is expected to oversee barley breeding efforts within the program under the supervision of the PI, and serve to facilitate barley related research projects related to development of new barley varieties adapted to the eastern United States.
The successful applicant is expected to apply genomic modeling techniques to phenotypic, genomic and environmental data to drive breeding decision making, with specific emphasis on research for combining agronomic performance with superior malting quality characteristics. The successful applicant will promote teamwork and help to manage students in barley research projects. The applicant will also serve to mentor graduate and undergraduate students conducting research in small grains.

REQUIRED QUALIFICATIONS

Requirements: Minimum M.S. degree in plant or animal breeding, population or quantitative genetics, and experience in a crop breeding program. Ability to work as a team member, excellent oral and written communication skills, demonstrated ability to present information in public forums, excellent time management and organizational skills, and the ability to work safely in an agricultural environment. Must have experience in cultivar development.

PREFERRED QUALIFICATIONS

Experience in cultivar development, knowledge in mixed models, genomic prediction, sequence alignment and variant calling methods, proximal sensing, significant programing proficiency in a suitable scientific language (Python, Julia, R, C, C++, Fortran, etc.), machine learning, and knowledge or familiarity of crop management and field research techniques. Demonstrated leadership and personnel management skills.

Please refer the Job description for details