Overview
The Cognitive Robot Autonomy and Learning (CoRAL) Lab at Purdue University is offering multiple Ph.D. positions for aspiring researchers interested in the innovative field of physics-informed robot learning. This opportunity is set to commence in Fall 2026, under the guidance of Prof. Ahmed Qureshi. The lab focuses on developing advanced robot learning methodologies that utilize partial differential equations (PDEs) and numerical principles, aiming to enhance robot capabilities with minimal supervision.
Background & Relevance
Robot learning is a critical area within artificial intelligence and robotics, emphasizing the development of algorithms that enable robots to learn from their environments and experiences. Physics-informed approaches are particularly significant as they integrate physical laws into learning processes, thus improving efficiency and effectiveness. This research is vital for applications such as robot manipulation, humanoid planning, and decision-making in dynamic settings, making it a cornerstone of future robotic advancements.
Key Details
- Start Date: Fall 2026
- Location: Purdue University, West Lafayette, IN
- Research Focus: Physics-informed robot learning for planning and control
- Application Portal: Purdue CS Graduate Admission Portal
- Preferred Advisor: Prof. Ahmed Qureshi
Eligibility & Participation
These Ph.D. positions are open to candidates with a strong background in computer science, robotics, or related fields. Applicants should demonstrate a keen interest in physics-informed learning and its applications in robotics. This opportunity is ideal for those looking to contribute to cutting-edge research in robot autonomy and learning.
Submission or Application Guidelines
Interested candidates should follow these steps to apply:
1. Visit the Purdue CS Graduate Admission Portal.
2. Complete the application for the Fall 2026 term.
3. Indicate Prof. Ahmed Qureshi as a preferred advisor in the application.
Additional Context / Real-World Relevance
The integration of physics-informed methodologies in robot learning is poised to revolutionize the field by reducing the need for extensive trial-and-error learning. This approach not only enhances the efficiency of learning processes but also ensures that robots can operate effectively in complex and dynamic environments. As robotics continues to evolve, such research will play a pivotal role in shaping the future of autonomous systems.
Conclusion
This is an exciting opportunity for prospective Ph.D. candidates to engage in groundbreaking research at the intersection of robotics and machine learning. Interested individuals are encouraged to explore this opportunity further and consider applying to join the CoRAL Lab at Purdue University.
Category: PhD & Postdoc Positions
Tags: robotics, machine-learning, physics-informed-learning, purdue-university, humanoid-robotics, reinforcement-learning, robot-manipulation, control-systems
