AI, Sensor Fusion & Precision Phenotyping Intern

Join Vylor—Powering the Future of Agriculture​ At Vylor, we’re advancing agriculture through breakthrough science—combining elite germplasm, cutting-edge biotech, and next-generation expertise in gene editing and molecular breeding. Our portfolio includes trusted industry brands like Pioneer®, Brevant®, and Hoegemeyer®, delivering performance farmers rely on.​ Join us to help scale innovation globally and shape what’s next in crop science. Vylor is seeking an AI, Sensor Fusion & Precision Phenotyping Intern for a 3-month research internship starting Summer 2027. You will join our Artificial Intelligence & Breeding group and help develop field-deployed perception systems that turn raw sensor data into quantitative plant measurements at scale. The work spans computer vision, 3D computational geometry, LiDAR and multi-modal sensor fusion, and machine learning, applied to data collected by ground robotic platforms in real breeding environments. Expect meaningful ownership of an R&D project from problem framing through algorithm development, field validation, and technical communication.

Key Responsibilities

Extract quantitative plant traits from multi-modal field imagery—RGB, LiDAR, multispectral, hyperspectral, thermal, and IMU/GNSS—using 3D computational geometry and sensor-fusion techniques. Implement and execute repeatable, well-documented workflows that apply established computer vision and machine learning methods with sensor-fusion-driven pre- and post-processing, delivered as modular components and scalable pipelines. Own a scientific research project with direct plant breeding impact: design experiments, quantify accuracy and failure modes, collaborate with breeders, geneticists, and engineers, and communicate results to technical and business audiences.

Qualifications

Currently pursuing an MS or PhD in Computer Science, Computer Engineering, Robotics, Electrical Engineering, Agricultural/Biosystems Engineering, or a related field; PhD candidates preferred. Enr