DC 3 - Integrating data-driven (AI) and physics-based modelling approaches for improved understanding of functional brain age

In SilicoHealth 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.

Who are we? The STADIUS-BIOMED research group is one of the world-leading groups developing and validating AI approaches in Healthcare, with applications covering various clinical disciplines.

The group is a friendly, close-knit collaborative team focused on delivering novel innovations into healthcare practice.

We closely collaborate with colleagues in UZ Leuven and with various industry partners, and have prior expertise in deploying methodology in clinical applications.

(https://biomed-kuleuven.web.app/; https://scholar.google.com/citations?hl=nl&user=xTjDXdQAAAAJ)
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Project

This PhD project will focus on generating a hybrid computational model with a possible application for estimating brain age. The objectives are: 1) Exploit the synergy between model-driven and data-driven approaches to model brain activity; 2) Integrate longitudinal functional data (e.g., multiple measurements of electrophysiology) to continuously update and personalise the brain model; 3) Develop an accurate and dynamic patient risk stratification tool for patients (e.g. differentiate healthy aging and patients with neurodegenerative disorders); 4) Increase explainability of models for an improved understanding on the changes in properties of the brain under healthy aging and neurodegenerative conditions (e.g., Alzheimer and Parkinson)

A successful project will result in novel hybrid models of the brain with improved performance and reduced computational requirements, the development of a dynamic clinical tool based on longitudinal clinical functional data, and clinical support tools for health professionals.
 
Planned secondments  :
· FAU (October year 2, 6 months): Aims to allow the DC to gain knowledge into the mechanical aspects of brain modelling, leading to integration of neuronal aspects in the hybrid modelling approach for this project.
· Sano (August year 3, 4 months): Allows the DC to gain more in depth knowledge on the state-of-the-art computational methods at a leading center for computational personalized medicine.

Profile

· You have completed a master’s degree in computer science, electrical engineering, applied mathematics/physics or biomedical engineering or possess corresponding qualifications that could provide a basis for successfully completing a doctorate.
· Experience in biomedical data analysis will be beneficial.
· You have a keen interest in Artificial Intelligence methodology and biomedical applications.
· 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 data-driven models to explore relationship between real age and estimated age and/or integrating physics constrains in data-driven models is not required but considered a plus

Offer

4 year PhD position in a highly dynamic European training network

Interested?

KU Leuven strives for an inclusive, respectful and socially safe environment. We embrace diversity among individuals and groups as an asset. Open dialogue and differences in perspective are essential for an ambitious research and educational environment. In our commitment to equal opportunity, we recognize the consequences of historical inequalities. We do not accept any form of discrimination based on, but not limited to, gender identity and expression, sexual orientation, age, ethnic or national background, skin colour, religious and philosophical diversity, neurodivergence, employment disability, health, or socioeconomic status. For questions about accessibility or support offered, we are happy to assist you at this email address.