PhD Fellowship: Computational Predictive Models of Skeletal Muscle Remodelling for Regenerative Robotics Applications
Are you passionate about advancing rehabilitation robotics? Do you want to help create predictive computer models that can reshape how we approach rehabilitation following severe neuro-muscular injuries?
Join us at the Neuro-Mechanical Modeling and Engineering Lab, where we're pushing boundaries in muscle neurophysiology, biomechanics, and robotics as part of the ERC Consolidator Grant ROBOREACTOR.
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.
Project Overview
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 force dynamometry, you'll bridge biomechanics and neurophysiology, driving novel insights in muscle modelling and rehabilitation.
Key Responsibilities
As part of our team, you will:
- Develop a computational muscle model, particularly for leg muscles, that simulates biological remodelling over time based on strain stimuli.
- Use high-density EMG, ultrasound, and force dynamometry to personalize models to reflect individual neuromuscular physiology.
- Program model remodelling logics in languages such as C++ and Python.
- Train machine learning algorithms to identify the most probable muscle remodelling processes based on strain data.
- Validate the model with both healthy and stroke patients, as well as through in vitro muscle data.
Collaborate with experts in control engineering, robotics, and bioengineering to contribute to developing a rehabilitation robotic system capable of autonomous tissue regeneration.
Information and application
Apply by November 28th, 2024. Applications must include the following documents:
- A video (2-minute max) describing your scientific interests and why you want to apply for this position.
- A cover letter (1-page max) specifying how your experience and skills match the position as well as summarizing work in your masters.
- A CV including English proficiency level, nationality, visa requirements, date of birth, experience overview, and publication list.
- Contact information for at least two academic references. A support letter will be requested only if your application is considered.
The first-round interview will be scheduled in the week of December 9th.
For questions, please contact Prof. Massimo Sartori, mail: m.sartori@utwente.nl.
Please, only apply via the web platform and not via email.
About the organisation
The Faculty of Engineering Technology (ET) engages in education and research of Mechanical Engineering, Civil Engineering and Industrial Design Engineering. We enable society and industry to innovate and create value using efficient, solid and sustainable technology. We are part of a ‘people-first' university of technology, taking our place as an internationally leading center for smart production, processes and devices in five domains: Health Technology, Maintenance, Smart Regions, Smart Industry and Sustainable Resources. Our faculty is home to about 2,900 Bachelor's and Master's students, 550 employees and 150 PhD candidates. Our educational and research programmes are closely connected with UT research institutes Mesa+ Institute, TechMed Center and Digital Society Institute.
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