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Texas A&M University

PhD Graduate Research Assistantship in AI-Enabled Robotics for Agricultural Automation Texas A&M University in United States

Degree Level

PhD

Field of study

Computer Science

Funding

Full funding available
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Country

United States

University

Texas A&M University

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Keywords

Computer Science
Environmental Science
Agriculture
Mechanical Engineering
Electrical Engineering
Artificial Intelligence
Agricultural Engineering
Navigation
Gear Design
Robotics
Embedded System
Control System
Machine learning

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About this position

Texas A&M University’s Controlled Environment Agriculture (CEA) Engineering Lab in the Department of Biological and Agricultural Engineering is seeking a highly motivated PhD student for research on AI-enabled robotics for agriculture.

The project focuses on the design and development of robotic and AI-driven systems for agricultural automation, with emphasis on intelligent control, mechanical design, simulation-to-real transfer, and digital twin modeling. Research activities include robotic hardware integration, end-effectors, mobile platforms, embedded control systems, ROS/ROS2, Isaac Sim/Gazebo, perception, manipulation, navigation, sensor-actuator integration, and collaborative experimental studies.

The student will begin at the Texas A&M main campus in College Station for coursework and later transition to the Texas A&M AgriLife Research Center in Dallas for research.

This is a PhD Graduate Research Assistant opportunity with competitive stipend, tuition, and medical benefits. The required background is an MS in Agricultural, Mechanical, Electrical, Robotics, or a closely related engineering discipline. Preferred skills include Python, C++, ROS/ROS2, Isaac Sim, and AI/ML for modeling, perception, or decision-making.

To apply, email a single PDF containing a cover letter, CV with TOEFL/IELTS score, academic transcripts, and representative publications to Dr. Azlan Zahid at [email protected].

Keywords: agricultural engineering, robotics, digital agriculture, artificial intelligence, machine learning, control systems, mechanical design, simulation-to-real transfer, digital twin modeling, ROS/ROS2, perception, manipulation, navigation, embedded systems.

Funding details

Full funding including tuition fees and living expenses is available for this position. The scholarship covers all educational costs and provides a monthly stipend.

How to apply

Please submit your application including a cover letter, CV, academic transcripts, and contact information for two references. Applications should be sent via the online portal before the deadline.

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