DC 5 – Physics-based and experimental data-driven arterial disease modelling

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
TU Delft, The Netherlands
Supervisory team
Dr Mathias Peirlinck (PhD main supervisor, TUDelft), Prof. Nele Famaey (PhD co-supervisor, KU Leuven), Prof.
Patrick Segers (PhD co-supervisor, UGent), Esteban Solano Montenegro (secondment host, Occlutech).
Enrolment in Doctoral School
Enrolled in the Graduate School of the TU Delft Faculty Mechanical, Maritime and Materials Engineering (TU
Delft) and the Arenberg Doctoral School for Science, Engineering & Technology (KU Leuven).

3. PhD project description:

Cardiovascular disease remains a leading cause of morbidity and mortality worldwide. The advent of in silico models has provided unprecedented opportunities for understanding, diagnosing, and treating these conditions through patient-specific simulations. However, the current deterministic nature of these models presents a significant barrier to their widespread adoption by industry and clinicians. Deterministic models often fail to capture the inherent variability and uncertainties present in biological systems, which can lead to misinterpretations and suboptimal clinical decisions.

In this project, you will address these challenges by developing robust methods for uncertainty quantification and propagation within virtual human twin models of cardiovascular disease. More specifically, you will develop a systematic framework to quantify the impact of inherent inter-sample and -subject variability associated with experimental tissue tests, the intrinsic uncertainty of in vitro and in vivo imaging techniques, and the effect of noisy experimental and clinical measurements on computational models of the diseased heart and aorta.

This project entails a joint doctoral degree between Delft University of Technology and KU Leuven. The research will be conducted in TU Delft’s department of BioMechanical Engineering under the supervision of dr. ir. Mathias Peirlinck, and KU Leuven’s department of Mechanical Engineering division of Biomechancs (BMe) under the supervision of prof. dr. ir. Nele Famaey. More information on both research groups can be found on
https://peirlincklab.com/andhttps://www.mech.kuleuven.be/en/bme/research/soft-tissue-biomechanics.

4. Planned secondments:

  • KU Leuven: Focused on gaining knowledge in experimental tissue testing techniques and deterministic material
    model fitting procedures currently used in the lab.
  • Occlutech : Aimed to provide exposure to cardiac implants and the importance of uncertainty quantification
    towards the in silico prediction of in vivo device behavior during and after implantation.

5. Essential requirements:

  • You have demonstrable experience with nonlinear continuum mechanics, finite element analysis, cardiovascular
    modeling, computational soft tissue biomechanics and cardiovascular (patho)physiology.
  • Affinity with scientific machine learning, Bayesian inference, data-driven modeling, and/or numerical analysis
    of PDEs and ODEs on complex domains is highly appreciated.
  • You have an excellent master’s degree (or an equivalent university degree) in Biomedical Engineering, Mechanical
    Engineering, Aerospace Engineering, Computational Physics, Applied Mathematics or a related field.
  • You are ambitious, well organized, a team player, and have excellent communication skills.
  • You can work independently, are a quick learner, and have a critical research-oriented mindset.
  • You are a pro-active and motivated person, eager to participate in network-wide training events, international
    mobility, and public dissemination activities.
  • You can effectively communicate scientific ideas, and foster collaborations in a highly multidisciplinary
    team.
  • You have excellent spoken and written English* language skills (minimum C1 level).

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.

*Doing a PdhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.