Milad Nazarahari

The IDEA Lab, led by Dr. Milad Nazarahari, and the Telerobotic & Biorobotic Systems Group, led by Dr. Mahdi Tavakoli, at the University of Alberta are recruiting a highly motivated PhD student for a fully funded position focused on intelligent, personalized training systems. This interdisciplinary project sits at the intersection of robotics, motor learning, and AI-driven human-interaction systems, with applications in rehabilitation therapy and professional skill learning. Research topics include human–robot interaction, motor learning and adaptive feedback, exergame/therapy personalization, and LLM-based interactive coaching. The successful candidate will work with state-of-the-art robotic systems such as Kinarm and ALEx Exoskeleton, and advanced sensing technologies including EEG, EMG, eye tracking, GSR, and HR. The project aims to develop next-generation adaptive training systems by integrating modules for real-time game difficulty modulation, task progression optimization, adaptive robotic support, feedback adaptation, and AI agents for interactive coaching. Applicants should have an MSc in Biomedical, Mechanical, Electrical Engineering, Computer Science, Kinesiology, or a related field, with strong interest and experience in human motor learning/adaptation, computational modeling, human-robot interaction, machine learning or AI systems, and data acquisition/analysis from physiological sensors. Proficiency in Python and ML/AI frameworks (PyTorch, TensorFlow) is required, along with excellent communication and teamwork skills. The position is fully funded, though specific financial details are not provided. Applications are accepted exclusively via the provided Google Form and will be reviewed on a rolling basis until filled. Early applications are encouraged. For more information, visit the IDEA Lab and Telerobotic & Biorobotic Systems Group pages.

The IDEA Lab at the University of Alberta is recruiting a PhD student to work on intelligent control systems for human training and rehabilitation . This opportunity sits at the intersection of control theory , robotics , biomedical engineering , neuroscience , and computer science , with a strong emphasis on adaptive systems that learn with the human during interaction. The research aims to move beyond traditional control paradigms by integrating motor learning , neurophysiology , and computational neuroscience into human-in-the-loop and learning-aware control frameworks. The successful student will help develop advanced control strategies for robotic rehabilitation, including adaptive, nonlinear, and optimal control, as well as hybrid architectures that combine model-based methods with AI-driven approaches. Research topics include: human-in-the-loop control, learning-aware control, neurophysiology-informed adaptation, state estimation, system identification, dynamic modelling, machine learning for control, reinforcement learning, and human-robot systems. The lab is especially interested in applications to physical rehabilitation, where affordable and accessible robotic systems can improve outcomes for individuals with neurological and musculoskeletal injuries. Ideal applicants should have a strong background in control theory and practical experience building controllers for robotic or mechatronic systems. Experience with intelligent systems, biomechanics, rehabilitation engineering, motor control, or neurophysiology is a strong advantage. The work is interdisciplinary and involves collaboration with engineers and clinicians, using rehabilitation robots and multimodal sensing technologies such as EEG , EMG , and IMU . This is a PhD opening in the Department of Mechanical Engineering at the University of Alberta . The post does not mention a deadline, and applications are to be submitted via the Google Form provided in the post. Emails will not be considered.

Milad Nazarahari, Assistant Professor at the University of Alberta, is still recruiting for fully funded PhD positions in the IDEA Lab within Mechanical Engineering . The openings are centered on three research directions: 1) Intelligent Adaptive Control for Robotic Rehabilitation — control theory, intelligent control, reinforcement learning, human-in-the-loop systems, and rehabilitation robotics. 2) Biosignal Processing During Human–Robot Interaction — wearable sensing, EEG/EMG/GSR, engagement and motivation estimation, and AI-driven rehabilitation. 3) Computer Vision for Human Movement Analysis — markerless pose tracking, image/video analysis, deep learning, RGB/RGB-D systems, and movement assessment. This is a student opportunity for applicants interested in robotics, computer vision, biomedical engineering, and AI methods applied to rehabilitation and human movement. The post emphasizes strong applicants and outstanding candidates, but does not list detailed GPA, degree, or language requirements. Funding is explicitly stated as fully funded . Applications are reviewed on a rolling basis , so candidates should apply as soon as possible. To apply, use the relevant form for the research area of interest. The poster notes that emails will not be considered , so applicants should not contact the professor by email for this opportunity.