Ageing-associated movement primitives defining healthy versus degenerative joint health

This position relates to InSilicoHealth (https://insilicohealthproject.eu/), an innovative Doctoral Network (DN) with the ambition to train a new generation of outstanding Doctoral Candidates (DC) that will become effective translators of the rapidly evolving digital technology to tackle existing and future challenges related to healthy aging in Europe. The research focus of this DN lies in three key domains: the brain, heart, and musculoskeletal (MSK) systems. In the realm of digital technology, InSilicoHealthspecifically focuses on virtual human twin (VHT) technology to enhance our understanding of the age-related adaptive changes of the complex human body through predictive multi-scale simulations. The research methodology employs knowledge-driven models enhanced by advanced data-driven inference techniques to optimize the health potential of older individuals.
This project is coordinated by iSI Health, the KU Leuven Institute for Physics-based modeling for in silico Health (iSiHealth.org), which unites researchers who aim to advance in silico technologies for use in healthcare-related topics.
This particular project is within the core of the research activities of the human movement biomechanics research group and, in particular, the joint mechanobiology team (https://gbiomed.kuleuven.be/english/research/50000737/research/HMB/Research/clinicalprojectsmain/ClinicalBiomechanics). It is our research ambition to study mechanical loading in different musculoskeletal tissues during normal and pathological movement and relate it to tissue adaptation, in particular, we aim to examine the role of mechanical loading in cartilage degeneration and osteoarthritis (OA) development using in vivo, in silico and in vitro approaches.
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Project

Are you passionate about biomechanics and eager to make a meaningful impact on joint health? Join our cutting-edge PhD project and contribute to groundbreaking research aimed at identifying movement-related signatures that predispose individuals to degenerative joint diseases.

What You’ll Do:

• Utilize advanced technologies like Opencap to analyze knee and hip joint movements from smartphone videos.

• Develop innovative computational models using probabilistic principal component analysis and statistical shape modeling.

• Collaborate with leading experts during secondments at TU Delft and Materialise Motion, gaining specialized skills in probabilistic modeling and dynamic gait measurement systems.

• Apply machine learning techniques to uncover disease-sensitive biomarkers for cartilage degeneration.

Why This Project?

This PhD position offers a unique blend of academic research and industry experience, providing you with the tools to develop a novel hybrid modeling workflow that incorporates parameter uncertainty. You will be at the forefront of creating a systematic, multidisciplinary approach to estimating joint contact pressures, paving the way for advanced clinical assessments and improved patient outcomes.

Key Highlights:

• Innovative Research: Focus on cutting-edge methods combining computer vision, deep learning, and musculoskeletal simulation.

• Collaborative Environment: Engage with top institutions like TU Delft and industry leaders at Materialise Motion.

• Impactful Outcomes: Enhance the understanding of biomarkers for cartilage degeneration, contributing to better prevention and treatment strategies.

Embark on a transformative journey that bridges computational techniques with real-world clinical applications. If you’re ready to push the boundaries of biomechanics research and develop solutions that matter, apply now!

Profile

• You have completed a master’s degree in biomedical engineering, bioengineering, movement sciences or possess corresponding qualifications that could provide a basis for completing a doctorate.
• Specialization in rigid body modeling, simulation, and finite element analysis will be beneficial.
• You have a keen interest in hybrid modeling, which combines rigid body and finite element simulations with machine learning approaches.
• You have proven your proficiency in English language equivalent to B2 level.
• You did not reside or carry out your main activity (work, studies, etc.) in Belgium for more than 12 months in the three years before 1st of January 2025.
• You are ambitious, well organized, a team player, and have excellent communication skills.
• You can work independently and have a critical mindset.
• You are proactive and motivated, eager to participate in network-wide training events, international mobility, and public dissemination activities.
• Previous experience in probabilistic principal component analysis, statistical shape modeling, surrogate modeling for uncertainty quantification, and/or smart regression analysis to identify biomarkers is not essential but is considered a plus.

Offer

We offer an exciting fully funded PhD position for 4-years in the context of a EU-funded MCSA JDN program 'InSilicoHealth' starting from January 6th, 2025.

Interested?

For more information please contact Prof. dr. Ilse Jonkers, tel.: +32 16 32 91 05, mail: ilse.jonkers@kuleuven.be or Prof. dr. Maarten De Vos, tel.: +32 16 37 39 97, mail: maarten.devos@kuleuven.be.

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