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.