Probabilistic failure risk assessment in ascending thoracic aortic aneurysms

Belgium Job Openings

KU Leuven

Leuven

FULL TIME

October 18, 2024

Probabilistic failure risk assessment in ascending thoracic aortic aneurysms

(ref. BAP-2024-639)
Laatst aangepast: 28/09/24

This research is part of In Silico Health (www.insilicohealthproject.eu), an innovative Doctoral Network with the ambition to train a new generation of outstanding Doctoral Candidates 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 doctoral network lies in three key domains: the brain, heart, and musculoskeletal systems. In the realm of digital technology, In Silico Health specifically focuses on virtual human twin 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 entails a joint doctoral degree between KU Leuven and Delft University of Technology. The research will be conducted in KU Leuven’s department of Mechanical Engineering division of Biomechancs (BMe) under the supervision of prof. dr. ir. Nele Famaey, and TU Delft’s department of Bio Mechanical Engineering under the supervision of dr. ir. Mathias Peirlinck. More information on both research groups can be found on https://www.mech.kuleuven.be/en/bme/research/soft-tissue-biomechanics and https://peirlincklab.com/

Website van de eenheid

Project

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.

This Ph D project will focus on defining a prospective patient-specific failure criterion for ascending thoracic aortic aneurysms, based on non-invasive patient measurements and retrospective data of patients. The objectives of the project are: 1) Create a framework to estimate a patient-specific probabilistic risk of rupture based on biomechanical criteria to improve the outcome of clinical decision-making; 2) Perform uncertainty quantification and uncertainty propagation activities for the computational framework; 3) Define a surgical decision-making framework.

A successful project will improve failure criterion for ascending thoracic aortic aneurysms correlating to patient-specific risk factors replacing the current generic maximum diameter criterion approach, will provide a novel hybrid modelling approach for the computational workflow, improve our understanding of the impact of uncertainty from patient-specific measurements on the computational framework and generate curated dataset of mechanical properties of the aorta in an elderly patient population.

The project will be mainly carried out at KU Leuven, however two secondments are planned during the project:

TUDelft (October year 2, 6 months): Focused on gaining knowledge on data-driven modelling techniques for integration into their framework.
Vascops (August year 3, 4 months): It will provide hands-on experience for the DC on an already commercially available digital interdisciplinary system combining medical image processing with biomechanical analysis for abdominal aneurysms.

Profile

You have completed a master’s degree in Biomedical Engineering, Mechanical Engineering, Aerospace Engineering, Computational Physics, Applied Mathematics, or a related field, or possess corresponding qualifications that could provide a basis for successfully completing a doctorate.
You have a keen interest in cardiovascular modeling, computational soft tissue biomechanics and cardiovascular (patho)physiology.
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 a pro-active and motivated person, eager to participate in network-wide training events, international mobility, and public dissemination activities.
Previous experience in hybrid modelling, multi-axial biomechanical tissue characterization, parameter optimization, nonlinear continuum mechanics, finite element analysis, constitutive model development and/or probabilistic modelling and multiple regression analysis is not required but considered a plus.

Offer

We offer a full-time (100%) doctoral fellowship for 4 years, embedded in a multidisciplinary team of researchers, and within a highly dynamic and international European training network.

Website of the In Silico Health doctoral training network

Interested?

For more information please contact Prof. dr. ir. Nele Famaey, nele.famaey@kuleuven.be or Dr. ir. Lauranne Maes, lauranne.maes@kuleuven.be.

You can apply for this job no later than November 01, 2024 via the online application tool

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.

Heb je een vraag over de online sollicitatieprocedure? Raadpleeg onze veelgestelde vragen of stuur een e-mail naar solliciteren@kuleuven.be

Solliciteer voor deze functie

av_timer Tewerkstellingspercentage: Voltijds
location_city Locatie : Leuven
timer Solliciteren tot en met:
01/11/2024 23:59 CET
bookmarks Tags: Ingenieurswetenschappen

We regret to inform you that this job opportunity is no longer available as it has expired

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