Postdoctoral Associate for High-Throughput Phenotyping in Maize
A postdoctoral associate position is available to work at the intersection of high-throughput plant phenotyping (HTPP) and statistical genetics in the Plant Breeding and Genetics Section. The position will be part of the NSF National Robotics Initiative (NRI)-funded project titled "Deep Learning UAVs for High-Throughput Agricultural Disease Phenotyping." This postdoctoral associate position involves the phenotyping of foliar diseases in maize with several complementary ground- and aerial-based methods in the field. The postdoc will computationally process collected images along with geospatial information and work with collaborators on the application of deep learning algorithms for reliable identification of foliar diseases. In addition, the postdoc will implement statistical genetic models for investigating trait heritabilities and correlations as well as studying quantitative trait loci (QTL) associated with disease phenotypes. The ideal candidate will have expertise in remote sensing, image processing, deep learning, and statistical genetics. Responsibilities will include research in the collection and processing of geospatial and image data, statistical dissection, prediction and validation of disease phenotypes, and training scientists and students. The position will involve close collaboration with a dynamic team of robotics engineers, computer scientists, statistical geneticists, and plant pathologists.
Position requirements
A Ph.D. in remote sensing, statistics, plant or animal breeding, genetics, or related discipline with at least 3 years of intensive training in statistical methods. Programming (R/Java/Python/Julia) and image (ImageJ/Agisoft/Pix4D) analysis skills, and working knowledge of remote sensing, geospatial, and statistical genetic approaches. Excellent interpersonal and communication skills with a strong publication record.
Preferred qualifications
A record of publication in the field of remote sensing and statistical genetics. Knowledge of plant breeding, genetics and pathology practices. Experience with the manipulation and analysis of highly dimensional data sets.
Supervision Exercised
The position will include some supervision of undergraduate students involved in research on the project.
A letter of interest in the position, C.V., and contact information for three references should be emailed to Michael Gore at: mag87 from cornell.edu<mailto:mag87 from cornell.edu>
Review of applications will begin immediately and continue until the position is filled.
Michael Gore
Associate Professor
Plant Breeding and Genetics Section
Cornell University
310 Bradfield Hall
Ithaca, NY 14853
E-mail: mag87 from cornell.edu
Vox: (607) 255-5492
Fax: (607) 255-6683
http://www.gorelab.us
