Drive forward the field of in silico medicine by leveraging physics-based and experimental data-driven techniques to improve our understanding of the ageing cardiovascular system.
Job 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.
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This research is part of InSilicoHealth (), 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 DN lies in three key domains: the brain, heart, and musculoskeletal systems. In the realm of digital technology, InSilicoHealth 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.
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).
Please highlight your specific skills and relevant prior experiences for this position explicitly in your motivation letter. Motivation letters that do not address any of these requirements will not be considered.
Conditions of employment
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1.5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2.5 years assuming everything goes well and performance requirements are met.
Application procedure
Are you interested in this vacancy? Please apply before
4 November 2024
- A 1-2 pages cover letter, explaining in a clear but concise way your motivations to apply to this position and how your CV and expertise fit the requirements.
- Curriculum vitae.
- A copy of your master's degree, including transcripts for your qualifying degrees (BSc, MSc).
- It is highly recommended to include samples of your work (a digital copy of your MSc thesis, reports, previous publications, videos, code, etc.).
Please note:
- A pre-employment screening can be part of the selection procedure.
- Please do not contact us for unsolicited services.
Het salaris bedraagt €2872 - €3670