1. Overview of the research programme:
InSilicoHealth is 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 with healthy ageing 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, InSilicoHealth
specifically 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.
2. Individual PhD Project Information:
- Host institution
- KU Leuven, Belgium
- Supervisory team
- Prof. Ilse Jonkers (PhD main supervisor, KU Leuven), Prof. Mathias Peirlinck (PhD co-supervisor, TU Delft), Dr.
Sam Van Rossom (secondment host, Materialise Motion) - Enrolment in Doctoral School
- Doctoral School of Biomedical Sciences, KU Leuven and Graduate School of Faculty the TU Delft Faculty 3mE.
3. PhD project description:
This PhD project will focus on identifying movement-related signatures of joint loading predisposing to degenerative joint disease
based on a large prospective cohort study. The objectives are to (1) perform data collection to obtain knee and hip joint kinematics
for a large elderly cohort (using Opencap, an open-source software combining computer vision, deep learning, and musculoskeletal
simulation to quantify human movement dynamics from smartphone videos); (2) derive movement primitives and associated ground reaction forces,
using probabilistic principal component analysis; (3) derive statistical shape modelling-based MSK models for the large elderly cohort;
(4) estimate contact pressures at the knee and hip joints for the elderly cohort; (5) quantify inherent uncertainty associated with input parameters for the contact pressure estimates, using a surrogate model approach; (6) construct SSM-based finite element models using contact pressures as inputs; (7) calculate cartilage degenerative markers; (8) determine disease-sensitive markers, using a machine learning approach such as smart regression algorithm.
A successful project will enhance our understanding of disease-sensitive biomarkers for cartilage degeneration in the knee and hip joints,
develop a novel hybrid modeling workflow that incorporates parameter uncertainty, and establish a systematic,
multidisciplinary computational approach to estimate joint contact pressures in these areas using smartphone video imaging.
4. Planned secondments:
- TU Delft Focused on acquiring expertise in methodologies for research activities 3-5.
- Materialise Motion: Aim to provide the DC with industry-focused experience in developing innovative dynamic gait
measurement systems for clinical assessments.
5. Essential requirements:
- You have completed a master’s degree in biomedical engineering, bioengineering, movement sciences or possess
corresponding qualifications that could provide a basis for successfully completing a doctorate. - Specialization in rigid body modeling and simulation, as well as finite element analysis will be beneficial.
- You have a keen interest in hybrid modeling – combining rigid body and finite element simulations, together 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 the host institution’s country 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 a pro-active and motivated person, eager to participate in network-wide training events, international
mobility, and public dissemination activities. - Previous experience in probabilistic principal component analysis, statistical shape modelling, surrogate
modelling for uncertainty quantification, and/or smart regression analysis to identify biomarkers is not
essential but considered a plus.
6. Application requirements:
- Curriculum vitae.
- Motivation Letter, including a clear indication of the preferred DC position(s) within InSilicoHealth Doctoral Network if the applicant postulates for multiple positions.
- Academic records (grades) for Bachelor and Master degrees.