Job offer

Chronic Heart Failure (CHF) affects more than 1.5 million people in France and is the leading cause of hospitalization for those over 65. The major challenge lies in the unpredictable nature of “decompensation” phases. Currently, detecting increases in intracardiac pressure - the precursors of a crisis - requires invasive examinations in a hospital setting.

The Normandeep M-Pulse project aims to transform this monitoring through the MyHeartSentinel system, consisting of a subcutaneous implant developed by the company CardiaMetrics. This implant records two types of key signals: the electrocardiogram (ECG) and the seismocardiogram (SCG).

The challenge is to develop an intelligent architecture capable of estimating intracardiac pressures from these signals, thereby enabling non-invasive, preventive, at-home monitoring., Under the supervision of the project managers and in close collaboration with the CardiaMetrics teams, you will work on the development and optimization of the model. Your main missions will be:

• Data Engineering: Description, preparation, and preprocessing of multi-source clinical databases (ECG and SCG signals from various pathological profiles).
• Research and Development:
• Optimization of the existing architecture for intracardiac pressure regression.
• Improvement of model sensitivity (detection of fine variations of ±3 mmHg).
• Research into inter-patient robustness (management of variability related to age, morphology, and comorbidities).
• Validation and Interpretability:
• Conducting rigorous tests to validate clinical performance.
• Explainability studies (XAI) and physiological correlation analysis to guarantee the medical reliability of the algorithm.
• Scientific Dissemination: Drafting scientific articles and presenting work at international conferences and specialized congresses (digital health, AI, cardiology).

Education Level: Master 2, Engineering Degree (Master’s level) or PhD in Computer Science, Research Field Computer science

Education Level Master Degree or equivalent

Skills/Qualifications

Education:

• Holder of a Master 2 in Computer Science, an Engineering Degree, or a PhD specialized in AI, Signal Processing, or Applied Mathematics and Digital Health.

Desired Skills:

• Experience in the biomedical field (knowledge of ECG/SCG signals).
• Skills in model deployment.
• Fluency in scientific English (written and oral).

Personal Qualities:

• Strong motivation for health challenges and medical innovation.
• Autonomy, scientific rigor, and intellectual curiosity.
• Ability to work in a collaborative environment (research/industry interface).