Research Fellow

8 hours ago

Singapore NATIONAL UNIVERSITY OF SINGAPORE Full time

Interested applicants are invited to apply directly at the NUS Career Portal Your application will be processed only if you apply via NUS Career Portal We regret that only shortlisted candidates will be notified.Job Description Prof Shuzhi Sam Ge's research group in the Department of Electrical and Computer Engineering at the National University of Singapore (NUS) is recruiting Research Fellows.Job Description The project focuses on the development of an intelligent switching control framework that integrates advanced modeling techniques, adaptive control algorithms, and self-learning capabilities for Modular Reconfigurable Mobile Robots (MR²) operating under diverse configurations and nonholonomic constraints.The successful candidate is required to:• Develop comprehensive kinematic and dynamic models of MR² robots under varying configurations and nonholonomic constraints;• Design and implement adaptive switching control strategies to ensure smooth transitions between control modes and physical configurations;• Analyze the controllability, observability, and stability of switched systems under feedback control schemes;• Incorporate machine learning or reinforcement learning techniques to enable self-adaptation and task generalization across different environments and payload conditions;• Contribute to the integration of simulation platforms and digital twins for control validation and performance evaluation;• Advance fundamental knowledge through high-quality journal publications, conference presentations, and patents;• Ensure timely preparation and delivery of project milestones and deliverables.Qualifications • Ph.D. Degree in Robotics, Control Engineering, Computer Science, Mechanical Engineering, or a related discipline;• Strong background in control theory, particularly in switched systems, adaptive control, robotic modeling, or dynamic systems;• Experience in mobile robot design, modular robotics, or nonholonomic system control is highly desirable;• Prior knowledge or demonstrated interest in reinforcement learning, hierarchical learning, or

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