- Virginia Tech
- Location: Blacksburg, VA
- Job Number: 7074719
- Posting Date: Feb 2, 2021
- Application Deadline: Open Until Filled
Job DescriptionJob Description
The School of Plant and Environmental Science at Virginia Tech is seeking a highly motivated postdoctoral associate to conduct research in modeling longitudinal growth and development and complex genotype-by-environment interactions in soft red winter wheat. The applicant would be a key part of a multi-disciplinary team with partners in other plant breeding research institutions and industry partners working in wheat, alfalfa, maize, cotton, soybean and canola. The successful applicant is expected to apply advanced modeling techniques to phenotypic, genomic and environmental data to estimate genetic parameters related to plant responses to environmental stresses and lead all aspects of sample analyses, UAV flights and management of wheat trials at Virginia Tech research stations. The position is anticipated to start in April 2021. The individual will be supervised by Dr. Nicholas Santantonio in the School of Plant and Environmental Sciences.
- Ph.D. in plant or animal breeding, population or quantitative genetics, statistics, computer science or computation-related fields with an interest in plant breeding applications. PhD must be awarded no more than four years prior to the effective date of appointment with a minimum of one year eligibility remaining.
- Ability to work as a team member, excellent 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 or be willing to acquire an FAA Part 107 drone pilot license within 1 to 2 months or a time frame agreed upon at hire. Funds to apply for a license will be provided.
- Incumbent must be willing to travel for up to two weeks for data collection at research and extension centers in eastern VA.
- Knowledge of mixed models, longitudinal modeling, sequence alignment and variant calling methods, proximal sensing and significant programing proficiency in a suitable scientific language (Python, Julia, R, C, C++, Fortran, etc.).
- Knowledge or familiarity of crop management, crop genetics, and field research techniques.