Industrial PhD Position – Reinforcement Learning for Wearable Robotics

Location: Barcelona, Spain (Expected start in 2027)

Academic partner: Institut de Robòtica i Informàtica Industrial (IRI, CSIC-UPC)

Duration: 4 years (Industrial PhD - subject to funding approval through the Spanish Industrial Doctorates programme)

About Us: ABLE Human Motion develops next-generation robotic exoskeletons to improve gait rehabilitation for people with neurological impairments. The ABLE Exoskeleton is a CE-marked medical device already deployed in rehabilitation hospitals across Europe, providing a unique opportunity to develop and validate cutting-edge robotics algorithms on a real clinical product.

• A fully industrial research environment developing a commercial medical robot
• Joint supervision by ABLE Human Motion and IRI (CSIC-UPC)
• Opportunity to publish in top robotics and AI conferences and journals
• Direct access to commercial robotic exoskeletons for experimental validation
• State-of-the-art simulation and computing infrastructure
• International multidisciplinary team
• Competitive salary according to the Industrial Doctorate programme

How to Apply? Send an email to [email protected] attaching your CV with the subject "PhD Reinforcement Learning".

We are seeking an outstanding PhD candidate to develop the next generation of intelligent control algorithms for lower-limb robotic exoskeletons using Reinforcement Learning.

The project will be carried out jointly between ABLE Human Motion and the Institut de Robòtica i Informàtica Industrial (IRI CSIC-UPC), one of Europe's leading robotics research institutes.

The objective is to develop adaptive locomotion and navigation algorithms capable of operating safely in both structured clinical environments and unstructured real-world settings.

Research topics include:

• Reinforcement Learning for locomotion control
• Safe Reinforcement Learning for physical human-robot interaction
• Sim-to-real transfer
• Human intention detection
• Adaptive gait assistance
• Robot navigation and obstacle avoidance
• Biomechanical modelling of human-exoskeleton interaction

The developed algorithms will be validated both in simulation and on commercial robotic exoskeletons with real users.

We are looking for highly motivated candidates with:

• MSc in Robotics, Artificial Intelligence, Computer Science, Control Engineering, Mechanical Engineering or related fields
• Strong programming skills (Python and C++)
• Experience with Reinforcement Learning or Machine Learning
• Robotics background, especially in motion control techniques such as optimal control or motion planning.
• Experience with ROS/ROS2 is highly valued
• Experience with simulation environments (Isaac Sim, MuJoCo or similar) is a plus
• Strong mathematical background
• Excellent English communication skills

Unlike many academic projects that stop at simulation, your research will be integrated into a real medical device used daily by patients in rehabilitation hospitals.

You will work at the intersection of reinforcement learning, wearable robotics, biomechanics and medical technology, contributing to the next generation of intelligent assistive robots with real clinical impact.