Mohammad Biglarbegian

Several funded PhD positions are available in the field of model-free control of unmanned aerial vehicles (UAVs) at the University of Ottawa and Carleton University, Canada. The positions are part of a collaborative research project focused on developing robust reinforcement learning (RL)-based controllers for autonomous UAVs, with an emphasis on online learning, formal Lyapunov-based robustness and stability guarantees, computational efficiency, and real-time performance. The research will provide opportunities for both analytical and hands-on work, publication in top journals, conference presentations, and mentoring junior researchers. Applicants should have a strong background in Lyapunov stability and nonlinear control theory, especially in optimal and adaptive control, as well as expertise in reinforcement learning and machine learning. Required skills include strong programming abilities in C/C++ or Python and Pytorch or TensorFlow, and familiarity with ROS/Gazebo. Excellent scientific writing skills in English and the ability to work collaboratively are essential. Experience with ArduPilot and UAV hardware is desirable but not mandatory. Successful candidates will join a dynamic team under the supervision of Prof. Wail Gueaieb (University of Ottawa) and/or Prof. Mohammad Biglarbegian (Carleton University). The Electrical and Computer Engineering program at the University of Ottawa and the Department of Mechanical and Aerospace Engineering at Carleton University are both involved in this project, offering a multidisciplinary environment at the intersection of electrical engineering, mechanical engineering, aerospace engineering, and robotics. The positions are fully funded, though specific stipend and tuition details are not provided. The target start date is September 1st, 2026, but early application is strongly encouraged due to administrative processing times, especially for international students. Applications will be reviewed on a rolling basis until all positions are filled. To apply, candidates should email a single PDF containing their CV (with publications and references), transcripts, and a cover letter detailing their fit for the project, particularly their expertise in Lyapunov-based stability analysis of data-driven control of nonlinear systems, to Prof. Wail Gueaieb at [email protected]. The subject line must follow the specified format. Only applications submitted according to these instructions will be considered.

Exciting fully funded PhD positions are available in the field of UAV control, reinforcement learning, and robotics at the University of Ottawa and Carleton University in Canada. The research focuses on developing data-driven, model-free controllers for unmanned aerial vehicles (UAVs), combining reinforcement learning with robust control theory to ensure real-time learning, adaptation, and stability guarantees. The project emphasizes both simulation and real hardware experimentation, with a strong focus on commercialization requirements such as Lyapunov-based robustness, computational efficiency, and real-time performance. Successful candidates will join a collaborative research environment, working under the supervision of Prof. Wail Gueaieb (University of Ottawa) and Prof. Mohammad Biglarbegian (Carleton University). The positions are part of a joint research initiative between the Electrical and Computer Engineering program at the University of Ottawa and the Department of Mechanical and Aerospace Engineering at Carleton University. Students will have opportunities to publish in top journals, present at major conferences, and mentor junior researchers. Applicants should have a strong background in Lyapunov stability, nonlinear control theory, reinforcement learning, and machine learning, with solid programming skills in C/C++ or Python and Pytorch or TensorFlow. Experience with ROS/Gazebo and scientific writing in English is required, while experience with ArduPilot and UAV hardware is advantageous. The positions are fully funded, covering tuition and providing a stipend, with additional support for research activities and conference travel. The application process requires submission of a CV, transcripts, and a cover letter detailing the candidate's fit for the research, especially in Lyapunov-based stability analysis of data-driven control of nonlinear systems. Applications should be sent to Prof. Wail Gueaieb at [email protected] with the specified subject line. The target start date is September 1, 2026, but applications are reviewed on a rolling basis and early submission is encouraged due to administrative processing times, especially for international students. Key research areas include UAV control, reinforcement learning, robotics, model-free control, autonomous systems, and real-time systems. This opportunity is ideal for candidates passionate about advancing the state-of-the-art in autonomous aerial vehicles and robust machine learning-based control systems.