Yiannis Karayiannidis

Two fully funded PhD positions are available in Control and Robotics at the Department of Automatic Control, Lund University, Sweden, as part of the ELLIIT strategic research environment. The research projects focus on (1) learning-based robotic mobile manipulation, developing safety-aware frameworks that combine reinforcement learning with control-theoretic structures for stable and adaptive manipulation in real-world environments, and (2) learning-based predictive decision-making for autonomous systems under uncertainty, advancing proactive decision-making in multi-agent systems using deep learning and optimization-based motion planning. Both projects are carried out in collaboration with Linköping University and are embedded in a vibrant, international research environment with strong ties to academia and industry. The department offers a stimulating atmosphere with PhD students, postdocs, and faculty from around the world, and values diversity and equality. The successful candidates will join RobotLab at LTH, a multidisciplinary experimental arena for robotics research. The ELLIIT initiative, funded by the Swedish government, supports strong research in information technology and mobile communications, with partners including Lund University, Linköping University, Halmstad University, and Blekinge Institute of Technology. Applicants should have a relevant MSc or equivalent degree, strong background in automatic control, robotics, and learning-based methods, and excellent English proficiency. The positions are fully funded with a competitive monthly salary (35,300 SEK), full employment benefits, and opportunities for professional development. The application deadline is 19 February 2026. For more information and to apply, visit the official Lund University job portal. Contact Associate Professor Yiannis Karayiannidis ([email protected]) for inquiries. Keywords: Control, Robotics, Reinforcement Learning, Autonomous Systems, Mobile Manipulation, Deep Learning, Optimization, Multi-agent Systems, Uncertainty Awareness, Motion Planning.