Linköping University is inviting applications for a fully funded PhD position in Automatic Control with a focus on learning robotics. The position is based in the Division of Automatic Control, Department of Electrical Engineering, and is supervised by Assistant Professor Farnaz Adib Yaghmaie. The research is part of a newly funded collaboration with Yiannis Karayiannidis and his team at Lund University, exploring innovative directions in robot learning, machine learning, and control.

The PhD project aims to develop learning-based methods that combine expert demonstrations with experiential reinforcement learning to enable robust, data-efficient skill acquisition for mobile manipulation. Research topics include leveraging multi-modal expert demonstrations (vision, force, proprioception) and experiential learning to handle complex tasks and adapt to new scenarios. The project is part of the ELLIIT initiative, 'Learning Robot Control from Expert Demonstrations and Self-Experience.'

Applicants should have a master’s degree (or equivalent) in electrical engineering, engineering physics, mechanical engineering, computer engineering, robotics, or engineering mathematics, with at least 240 credits (60 advanced). Required skills include a strong background in robotics and control engineering, analytical and problem-solving abilities, programming experience (Python, C++), and proficiency in English. Additional merits include knowledge of machine learning, reinforcement learning, optimization, experience with multi-modal sensor data, and simulation environments such as PyBullet, MuJoCo, Gazebo, or Isaac Sim.

The position is fully funded, with salary progression according to university policy. The employment is initially for one year, renewable up to four or five years depending on progress and departmental duties. The start date is by agreement, but no later than October 31, 2026. The application deadline is February 27, 2026.

To apply, submit your application via the Linköping University vacancies portal. For more information, contact Assistant Professor Farnaz Adib Yaghmaie or HR Coordinator Karin Blomdahl. The research environment is vibrant, with extensive collaborations and opportunities for teaching and professional development.

Keywords: robot learning, automatic control, reinforcement learning, machine learning, robotics, control theory, mobile manipulation, engineering, sensor fusion.