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Rehabilitation Robotics

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rehabilitation robotics

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3 curated items2 Positions1 Seminar
Updated 1 day ago
3 items · rehabilitation robotics
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Position

Massimo Sartori

University of Twente
University of Twente, TechMed Centre, Robotics Centre, Faculty of Engineering Technology, Department of Biomechanical Engineering, 7500 AE, The Netherlands
Dec 5, 2025

The Neuro-Mechanical Modeling and Engineering Lab (NMLab) at the University of Twente invites applications for a 3-year postdoctoral position funded by the ERC Consolidator Grant ROBOREACTOR. This is an exciting opportunity to join a cutting-edge team at the intersection of neurophysiology, biomechanics, and rehabilitation robotics. As a postdoctoral researcher in this project, you will work on breakthrough technology for non-invasive biopsies of skeletal muscles, specifically targeting the lower limbs. You will employ high-density electromyography (HD-EMG) and ultrasonography, combined with advanced statistical and machine learning techniques, to characterize muscle properties at multiple scales. Key focuses include motor unit phenotype distribution, 3D muscle fascicle morphology, and muscle inflammation levels. You will validate these non-invasive measurements against invasive biopsy samples and advanced imaging techniques, working with both healthy individuals and post-stroke survivors in the context of rehabilitation robotics and regenerative robotics technologies.

Position

Massimo Sartori

University of Twente
University of Twente, TechMed Centre, Robotics Centre, Faculty of Engineering Technology, Department of Biomechanical Engineering, 7500 AE, The Netherlands
Dec 5, 2025

This 4-year PhD position offers you the chance to work in an innovative interdisciplinary environment, collaborating on groundbreaking research at the frontier of healthcare and robotics. As a PhD fellow, you’ll play a central role in building a predictive, multi-scale model of human skeletal muscle. This model will simulate how motor units within muscles respond to neural signals discharged by spinal neurons and adapt structurally over time when subjected to specific physical strain regimens. Leveraging machine learning and statistical modeling, you’ll integrate data from in vivo and in vitro studies to accurately predict muscle remodelling. The model will be validated against data from both healthy participants and post-stroke patients following a targeted 12-week leg training protocol. Using advanced tools such as high-density electromyography, ultrasound, and robotic dynamometry, you'll bridge biomechanics, neurophysiology and robotics, driving novel insights in muscle modelling and rehabilitation.