The LTSI laboratory (INSERM 1099) opens a postdoc position in the field of computational modeling,
clinical data analysis and machine-learning, funded by the European project SMASH-HCM.
Context
Hypertrophic cardiomyopathy (HCM) is the most common form of genetic heart disease, characterized
by thickening of cardiac walls, increasing risks of arrhythmia, and represents a major cause of sudden
cardiac death (SCD), particularly in the young population, with a risk of about 1% per year.
Identification of patients at risk is still a major clinical challenge and novel methodological tools should
be proposed in order to improve disease management.
Goal
The main objective is to address the multifactorial nature of the disease, by analyzing clinical
databases composed of HCM patients, with different levels of risk, in order to identify novel potential
markers for risk stratification. The objective of the proposed approach will be to develop multiscale
dynamic vascular and multiorgan modelling and simulation tools to capture vascular and multiorgan
mechanisms for deep phenotyping in HCM. The autonomic function in HCM will be especially
evaluated through methods capturing heart rate complexity and variability. Hybrid methodology,
combining in silico models and signals processing, will be proposed. Model analysis methods will be
used to study the autonomic mechanisms regulating the mechanical and circulatory functions of the
cardiovascular system in this population and in silico patient-specific model-based features will be
extracted. Finally, multivariate approaches, based on supervised and unsupervised machine learning
methods, will be proposed to define robust classifiers capable of identifying patients at high risk. This
will enable the prediction of adverse events and prognostic outcomes and improve therapy choices.
Profile
We are looking for a highly motivated postdoctoral scientist with expertise in computational modeling
or biomedical data analysis. The applicant will have some skills either in computer sciences, signal
processing or applied mathematics. Experience in analysis computational model, machine-learning or
numerical computation will be highly appreciated.
Location / Hiring date
Rennes / January 2024 (could be adapted to availability)
Duration - 24 months
Contacts
- Virginie Le Rolle
Maître de conférences, HDR
virginie.lerolle@univ-rennes1.fr - Alfredo I. Hernandez.
Directeur de Recherche INSERM
alfredo.hernandez@univ-rennes.fr
| Fichier attaché | Size |
|---|---|
| SMASH_HCM.pdf | 90.21 Ko |