Computer Vision and Data Analysis Co-Op

Role: Data analyst specializing in digital phenotyping for crop field trials, leveraging computer vision and machine learning to support decision-making and optimize system workflows. Responsibilities: analyze and interpret imagery and metrics from UAV, remote sensing, and manual field assessments; collaborate with Field Testing and Data Science teams to integrate insights into models; contribute to experimental design and process improvements through advanced data techniques. Requirements: active M.S. or Ph.D. candidate in data science, precision agriculture, plant sciences, or related fields; proficiency in Python and deep learning frameworks like PyTorch or TensorFlow; strong understanding of machine learning concepts; familiarity with agricultural field trial methodologies and remote sensing; ability to work collaboratively. Preferred: hands-on experience with architectures such as ResNet, UNet, YOLO; dataset management skills; UAV imaging experience; cloud platform familiarity; FAA Part 107 license preferred. HighValue: focus on digital phenotyping in crop science, field trial analysis, UAV and remote sensing technologies, and collaboration across interdisciplinary teams. WorkSetup: hybrid in Chesterfield, Missouri, with regional travel to field sites in Illinois and potentially other Midwest and Midsouth locations.