Postdoctoral Fellow Opening: Reinforcement Learning for the Control of Human-Inspired Musculoskeletal Robots University of Twente in Netherlands

This postdoctoral fellowship at the University of Twente offers an exciting opportunity to advance reinforcement learning for the control of human-inspired musculoskeletal robots. The position is part of a collaborative SNSF Co-Investigator Grant between ETH Zurich’s SoftRobotics Lab and University of Twente’s NeuBotics Lab, focusing on digital twins of musculoskeletal robots equipped with neuronal control networks. The project aims to derive robust robot controllers for sim2real applications, bridging robotics, biomechanics, artificial intelligence, and neuroscience.

As a postdoctoral fellow, you will adapt human neuromusculoskeletal models to incorporate robotic limbs with muscle-like, variable-stiffness actuators. You will develop digital twins integrating neuromusculoskeletal and electrofluidic actuator models, and use reinforcement learning to train these twins for robust, human-like joint impedance control. The project also involves imitation learning frameworks, enabling robotic limbs to reproduce human movement and stiffness properties by observing human twins, and transferring RL-policies to real hardware.

Secondary tasks include collaborating with interdisciplinary researchers in biomechanics, robotics, and machine learning, and disseminating research through publications, open-source software, and international conferences. The NeuBotics Lab is a multidisciplinary team at the forefront of neuromechanics, robotics, and human movement science, offering a dynamic research environment focused on translating computational models into real-world robotic applications such as prosthetic limbs, wearable exoskeletons, and autonomous musculoskeletal robots.

Required qualifications include a PhD in Robotics, Computer Science, Artificial Intelligence, Control Engineering, Mechanical Engineering, Biomedical Engineering, Electrical Engineering, or a related discipline. Applicants should have a strong publication record in robotics, neuromechanics, reinforcement learning, or related fields, and hands-on experience with reinforcement learning, imitation learning, control of robotic systems, sim2real, machine learning frameworks (PyTorch or TensorFlow), GPU-accelerated simulation environments (MuJoCo, Newton), and programming in Python and/or C++. Excellent communication skills in English and the ability to work in an interdisciplinary environment are essential. Knowledge of soft robotics, electrofluidic actuators, prosthetic or assistive robotic technologies, and human movement analysis and biomechanics is a plus.

The offer includes a full-time 2-year position (with possibility of 6-12 months extension), a gross monthly salary between €4241 and €4728 depending on education and experience, a 30% tax ruling option, pension scheme, annual holiday allowance of 8% of gross annual salary, end-of-year bonus of 8.3%, and a minimum of 41 holidays per year for full-time employment. The University of Twente provides excellent mentorship, professional and personal development programs, and access to state-of-the-art neuromechanics, robotics, and AI-compute facilities. The campus is located in Enschede, a mid-size city in the Twente region, offering a vibrant social environment and proximity to nature.

Applications should be submitted via the University of Twente web platform by July 5, 2026. Required documents include a video describing your scientific interests, a cover letter detailing your experience and skills, a CV with English proficiency and publication list, and contact information for at least three academic references. For further information, contact Prof. Massimo Sartori at [email protected]. Please do not apply via email.

The Faculty of Engineering Technology (ET) at University of Twente is committed to developing smart, sustainable, human-centred technological solutions for societal challenges, connecting education, research, and practice across core domains such as asset & maintenance engineering, intelligent manufacturing systems, personalised health technology, resilience engineering, and sustainable production, energy and resources. The open community fosters collaboration between students, researchers, staff, and industrial partners, driving innovation and impact.