PhD offer - Development of a physiological model-based analysis and machine-learning approaches for risk stratification in hypertrophic cardiomyopathy

15 May 2024
Job Information

Organisation/Company

Université de Rennes

Department

Inserm, LTSI-UMR 1099

Research Field

Computer science » Programming
Medical sciences » Medicine
Mathematics » Applied mathematics
Technology » Medical technology

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

28 Jun 2024 - 18:00 (Europe/Paris)

Type of Contract

Temporary

Job Status

Full-time

Offer Starting Date

2 Sep 2024

Is the job funded through the EU Research Framework Programme?

HE

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Context

The LTSI (Laboratoire Traitement du Signal et de l'Image) is a research laboratory at the University of Rennes and INSERM, at the interface of information sciences and health technologies.

Description

Hypertrophic cardiomyopathy (HCM) is characterized by a hypertrophy of the cardiac muscle and represents one of the main causes of sudden cardiac death in young people. Identifying patients at risk is still a major clinical challenge. In addition to the electrocardiogram (ECG), the cardiac function can be evaluated by transthoracic echocardiography (TTE), which is an imaging technique that allows the visualization of myocardial deformations (strain). However, analyzing strain curves can prove difficult because their morphologies reflect both electrical conduction delays and modifications in the myocardium mechanical activity at the same time. In this context, the
use of mathematical models and machine learning methods can be relevant to integrate physiological knowledge in data analysis and to analyze the underlying  mechanisms.

Objective

This PhD project is included in the SMASH-HCM European project which aims at proposing stratification and therapeutic guidance tools for HCM patients by developing numerical twin solutions. The LTSI takes part in this project and leads the work package focusing on multiscale modeling. The team has previously proposed an integrated model of the cardiovascular system. The main objective of this PhD project is to create patient-specific models considering the cardiovascular regulation by the autonomic nervous system to produce individualized physiological markers of cardiac contraction. The models’ parameters will be identified based on a clinical database of 201 HCM patients. These patient-specific parameters can be used to interpret myocardial strain signals in these patients. They will also be used to identify the best therapy choice using machine learning methods.

Candidate profile

We are looking for a student who successfully completed her/his master's degree or engineering school, with skills in numerical analysis, signal analysis, programming (C++, python), and machine learning. The candidate must be motivated by biomedical engineering, and physiological knowledge, although not required, will be appreciated.
 

Location / start date / Duration

Rennes, Campus Beaulieu / 2024 / 3 years

Contacts
Joan Duprez, Associate Professor, [email protected]
Virginie Le Rolle, Associate Professor, HDR, [email protected]
Alfredo I. Hernandez., INSERM Research Director, [email protected]

Requirements

Research Field

Computer science » Programming

Education Level

Master Degree or equivalent

Research Field

Mathematics » Applied mathematics

Education Level

Master Degree or equivalent

Research Field

Medical sciences » Medicine

Education Level

Master Degree or equivalent

Research Field

Technology » Medical technology

Education Level

Master Degree or equivalent

Skills/Qualifications

We are looking for a student who successfully completed her/his master's degree or engineering school, with skills
in numerical analysis, signal analysis, programming (C++, python), and machine learning. The candidate must be
motivated by biomedical engineering, and physiological knowledge, although not required, will be appreciated.

Languages

ENGLISH

Level

Excellent

Languages

FRENCH

Level

Basic

Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

University of Rennes

Country

France

Geofield

Where to apply

E-mail

[email protected]

Contact

State/Province

Brittany

City

Rennes

Website

https://ltsi.univ-rennes.fr/

Street

Campus de Beaulieu. Bât 22

Postal Code

35133

STATUS: EXPIRED