PhD student in biomedical signal processing: « Graph characterization of multipolar electrograms in persistent atrial fibrillation using advanced simulation models » (M/F)

CNRS; France; Sur place; EUR 22 000 - 29 000; Plein temps

Résumé du poste

CNRS in France invites applications for a First Stage Researcher PhD position in engineering/computer science, hosted by the i3S Laboratory at Sophia Antipolis. The project spans cardiac signal processing, multiscale models, and graph signal processing, with collaboration across Karlsruhe KIT and partner hospitals in Nice and Monaco.

The thesis includes a 6-month research visit at KIT in Germany, under supervision of Vicente Zarzoso, and aims to deliver reproducible, explainable results to

Description du poste

Organisation/Company CNRS Department Laboratoire d'Informatique, Signaux et Systèmes de Sophia Antipolis Research Field Engineering Computer science Mathematics Researcher Profile First Stage Researcher (R1) Application Deadline 25 Sep 2026 - 23:59 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Jan 2027 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The i3S laboratory (https://www.i3s.univ-cotedazur.fr ) is a joint research unit between the CNRS and the Université Côte d'Azur with Inria as secondary regulatory authority. i3S was one of the first laboratories to be established in the Sophia Antipolis technology park and brings together about 300 people, including about a hundred lecturers-researchers mainly from 3 components of Université Côte d'Azur: Polytech Nice Sophia, EUR DS4H and IUT Nice Côte d'Azur. The laboratory also brings together 20 researchers from the CNRS and 13 researchers from Inria, not to mention about twenty staff from the technical and administrative teams. Nearly 90 doctoral students, a dozen post-docs, 60 master's or engineering school interns complete the workforce. Attached to the CNRS Institute of Computer Science, its research themes cover a wide spectrum of topics in CNU sections 27 "Computer Science" and 61 "Computer Engineering, Automation and Signal Processing". The laboratory is located at the heart of the Sophia Antipolis technology park, in a dynamic ecosystem that brings together academics and companies of all sizes.

The Signal team of the i3S Laboratory (https://i3s.univ-cotedazur.fr/signal ), aims to develop advanced, innovative and adapted tools for the processing of signals or images acquired with biomedical sensor networks (cardiology, neurosciences) or in geosciences (seismology and marine ecology), but also in wireless communications. The team specializes in multi-sensor methods, tensor decompositions and component analysis for the joint processing of multimodal data, notably in the context of invasive (intracardiac electrograms) and non-invasive (surface electrocardiogram) records of cardiac activity for the characterization of arrhythmia.

This interdisciplinary thesis will be carried out in the context of an international consortium composed of specialists in signal processing (i3S Laboratory), computational cardiac modeling (Karlsruhe Institute of Technology, Germany) and interventional cardiology (Nice Pasteur University Hospital and Monaco Princess Grace Hospital). The thesis will be supervised by Vicente Zarzoso (Full Professor, Université Côte d'Azur), coordinator of the collaborative research project GrAF funded by the ANR.

The thesis will include a 6-month stay at the Computational Cardiac Modeling (CaMo) group of Karlsruhe Institute of Technology (KIT), Germany, partner of the ANR GrAF project. Headed by Axel Loewe, CaMo@KIT is a leading research center in multiscale models for cardiac electrophysiology, with focus on simulating intracardiac electrograms as an enabling technology for arrhythmia characterization. CaMo@KIT will be instrumental in developing the advanced computational models of the atrial tissue required in the thesis' simulation study.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia encountered in clinical practice, linked to several complications including brain strokes. Currently, the most attractive therapy for persistent AF is catheter ablation, where catheters equipped with multiple electrodes – multipolar catheters – are increasingly used to facilitate electroanatomic mapping of the atria before ablation. Despite advances in ablation technology, the search for optimal patient-tailored protocols remains elusive, mainly due to the incomplete characterization of the abnormal propagation patterns maintaining the arrhythmia. In a bid to shed light on this open challenge, this PhD thesis will focus on the characterization of multipolar electrograms (EGM) through graph signal processing (GSP). The underlying hypothesis is that local propagation patterns in AF are associated with specific space-time signatures in multipolar EGM, and that such signatures can be captured by suitable GSP tools exploiting the catheter configuration. The thesis will assess the ability of GSP methods to describe AF propagation scenarios in realistic synthetic signals generated with advanced simulation models developed at the Computational Cardiac Modeling (CaMo) group, Karlsruhe Institute of Technology (KIT), Germany. GSP techniques will be validated and fine-tuned in a variety of simulation conditions. To test their clinical relevance, the optimized GSP techniques will also be applied to real data acquired during ablation procedures at two partner hospitals: Nice Pasteur University Hospital and Monaco Princess Grace Hospital. Well-grounded on interpretable mathematical concepts and state-of-the art computational models, the approach to be developed in the thesis is expected to yield reproducible, explainable results, increasing the reliability of clinical decision-making and paving the way for novel patient-tailored ablation protocols. As part of the thesis, the PhD candidate will spend 6 months at CaMo@KIT to actively take part in the generation of the synthetic signal dataset to be leveraged in the project.