PhD in AI and Robotics for Smart Weed Control and Plant Phenotyping Texas A&M University in United States

Texas A&M University is advertising a PhD opening in AI and Robotics for Smart Weed Control and Plant Phenotyping in the Department of Soil and Crop Sciences, College Station, Texas, USA.

The project is a PhD graduate research assistantship focused on developing AI/ML and robotic solutions for weed detection, plant phenotyping, and precision management. The student will be part of an interdisciplinary research team working with agronomists, breeders, and engineers, including collaboration with Dr. Kiju Lee in Mechanical Engineering at Texas A&M.

Research interests and keywords include precision agriculture, smart weed control, plant phenotyping, robotics, machine learning, computer vision, and embedded systems. The role is especially relevant for applicants interested in field robotics, autonomous platforms, high-throughput phenotyping, and precision spray systems.

Eligibility highlights: applicants should have or be completing an MS degree in a relevant field, though exceptional candidates with a BS degree and strong relevant experience may also be considered. Required skills include hands-on experience with robotic hardware (sensors, actuators, embedded systems), Python programming for robotics and ML, familiarity with computer vision and image analysis tools such as OpenCV and YOLO, and the ability to work in interdisciplinary teams. Preferred experience includes ROS, autonomous platforms, high-throughput plant phenotyping pipelines, and precision spray systems.

International applications are encouraged, but candidates currently based in the United States will be given preference. The post states that screening is immediate and the search will continue until a suitable candidate is identified. The position starts in the Summer/Fall 2026 term.

How to apply: email your CV, an unofficial academic transcript, and contact details of three referees to [email protected]. Include Field Robotics in the email subject line.