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: Pompeu Fabra University (UPF), Spain
Supervisory team: Prof. Jerome Noailly (PhD supervisor, UPF), Prof. Ilse Jonkers (PhD co-supervisor, KU Leuven), Prof. Miguel Ángel González Ballester (PhD co-supervisor, UPF), Dr Ludovic Humbert (secondment host, 3D-Shaper).
Enrolment in Doctoral School: Enrolled in the Information and Communication Technologies (UPF) and at the Doctoral School of Biomedical Sciences (KU Leuven).
3. PhD project description:
This PhD project will focus on coupling biological regulatory network and organ finite element models to define risk factors of different rates or organ ageing in personalised models related with patient (osteoarthritis, low back pain) and population cohorts, with which UPF works. The objectives are: 1) Couple pre-existing models at UPF: chondrocyte and intervertebral disc mechano-sensitive cell regulatory networks models with finite element models of the knee joint and the intervertebral disc; 2) Personalise the shapes of the organ models by combining magnetic resonance image segmentation and mesh morphing; 3) Personalise the regulatory network initial states, based on patient BMI, age and other factors known to control low grade inflammation mediators mapped in the networks; 4) Run simulations and mine together input data for model personalization and simulated data related with network node activations that reflect nociceptive pain, pro-inflammatory cytokine activity, balance between matrix proteases and inhibitors thereof, structural proteins; 5) Define a pipeline for model assessment, based on uncertainty and consistency analyses, falsification tests against clinical cases, capacity for discrimination in clinical case-control: 6) Assess risk factors and build corresponding surrogate models.
A successful project will result in a robust pipeline for multiscale modelling that allows mechanistic explorations of pathophysiological mechanisms and risk factor predictions for age-related joint degeneration, based on interpretable biological mechanisms.
4. Planned secondments:
- KU Leuven (December year 2, 6 months): Aims to personalise the mechanical boundary conditions to be imposed on the knee joint and intervertebral disc models, based on the movement signatures investigated by DC7, and on the translation thereof into mechanical loads to be applied on the joints, through existing collections of motion capture and MSK analyses at KU Leuven (knee joint), and through existing measurements of in vivo intervertebral disc pressure under daily activities (intervertebral disc).
- 3D-Shaper Medical (May year 1, 4 months): Early secondment at 3D-Shaper Medical aims to explore robust pipelines for personalised modelling of knee joints, through machine-learning based image processing allowing advanced annotations and fast 3D modelling, out of X-rays and MRI.
5. Essential requirements:
- You hold both a Bachelor’s and a Master’s degree in Biomedical Engineering, Biomedicine, Computer Science, Industrial Engineering, Mechanical Engineering.
- Specialization in computational methods in biomedical engineering or biomedicine will be highly beneficial.
- You have a keen interest in the fields of in silico medicine, digital health, and rheumatology.
- You have proven your proficiency in English language equivalent to B2 level (Sufficient English level will be verified during the interview, if any).
- 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 and analytical 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 finite element modelling, and/or medical image processing, and/or data science, and/or motion capture and analyses, are not required 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) of both the Bachelor’s and Master’s degrees.
- Two recommendation letters by two previous scientific supervisors (these people might be contacted by the Evaluation Committee of the position, if needed).
