⛔ This position is no longer available.
University: University of Rennes
Country: France
Deadline: Not specified
Fields: Biomedical Engineering, Electrical Engineering, Signal Processing, Machine Learning, Data Science
Are you passionate about harnessing the power of artificial intelligence and data fusion to revolutionize healthcare and save lives? If you are eager to contribute to groundbreaking research at the intersection of biomedical engineering, machine learning, and clinical cardiology, this opportunity at the University of Rennes could be the next step in your academic journey.
About the University or Research Institute
The University of Rennes, located in the vibrant city of Rennes in western France, is renowned for its excellence in research and innovation. As a leading public institution, it boasts a strong tradition in science and technology, with a particular emphasis on interdisciplinary collaboration. The Laboratoire Traitement du Signal et de l’Image (LTSI) at the University of Rennes is a premier research group specializing in signal and image processing, biomedical engineering, and health technology. The university’s dynamic academic environment, coupled with its strong ties to national research organizations such as INSERM and CNRS, makes it an ideal destination for aspiring researchers seeking to make impactful contributions to science and society.
France itself offers a rich cultural and academic landscape, with high-quality research infrastructure, competitive funding opportunities, and a supportive environment for international students. Living in Rennes provides access to a thriving student community, excellent public transport, and a high standard of living at a relatively affordable cost compared to other European cities.
Research Topic and Significance
The PhD position focuses on “Graph-Based Multimodal Data Fusion for Ventricular Arrhythmia Risk Prediction.” Cardiovascular diseases are the leading cause of mortality globally, and sudden cardiac death—often triggered by ventricular arrhythmias such as ventricular tachycardia or ventricular fibrillation—remains a critical challenge in clinical practice. Despite advances in diagnostics, existing risk stratification tools lack the sensitivity and specificity needed for accurate prediction, leaving high-risk patients vulnerable.
This research aims to overcome these limitations by integrating diverse data sources—imaging, electrocardiograms, and genetic markers—into unified, graph-based models. By developing a personalized digital twin of the cardiovascular system, the project seeks to enable more precise prediction and prevention of life-threatening arrhythmias, ultimately improving outcomes for high-risk populations, especially post-infarction patients.
Project Details
This PhD position is part of the INTERACTION project, a flagship initiative under the French PEPR Digital Health program. The project involves collaboration between several leading French institutions, including:
– LTSI – INSERM U1099 (University of Rennes)
– IADI – INSERM U1254 (University of Lorraine)
– Thorax Institute – INSERM U1087 / CNRS U6291 (University of Nantes)
The successful candidate will join the LTSI lab at the University of Rennes and contribute to the development of a next-generation, AI-driven framework for cardiovascular risk prediction. The research will focus on designing and implementing graph-based strategies to fuse multimodal cardiac data—mechanical, electrical, and imaging information—and developing patient-specific models for ventricular arrhythmia risk assessment. The ultimate objective is to construct a digital twin of the heart that can inform personalized clinical decisions.
This position was advertised by the project supervisor on LinkedIn. For more information and application instructions, refer to the original LinkedIn post:
https://www.linkedin.com/posts/ahmad-karfoul-25348648_phd-position-ugcPost-7459710370237251585-DI9d
Candidate Profile
The ideal candidate for this position will demonstrate:
– A Master’s degree (or equivalent) in biomedical engineering, electrical/electronic engineering, or a closely related field.
– Strong background in signal processing.
– Experience with machine learning or deep learning; familiarity with graph neural networks is highly desirable.
– Proficiency in numerical optimization techniques.
– Excellent Python programming skills.
– Motivation to work at the interface of engineering, data science, and clinical medicine, with a commitment to advancing healthcare technologies.
Candidates should be self-driven, collaborative, and eager to engage with multidisciplinary teams spanning engineering, medicine, and computer science.
Application Process
The position is expected to start on October 1, 2026. The application deadline is not specified in the advertisement. Interested applicants should consult the official LinkedIn post for further details on how to apply:
https://www.linkedin.com/posts/ahmad-karfoul-25348648_phd-position-ugcPost-7459710370237251585-DI9d
Conclusion
If you are excited about pushing the boundaries of digital health and biomedical AI, this PhD opportunity at the University of Rennes offers a unique chance to work on high-impact research with leading experts in the field. Don’t miss the opportunity to contribute to the future of personalized medicine and improve patient outcomes. For more positions like this, follow our updates and explore related opportunities below.
Questions & Answers
Question: What makes France an attractive destination for pursuing a PhD in biomedical engineering?
France is known for its world-class research institutions, generous funding schemes, and multicultural academic environment. Students benefit from access to cutting-edge facilities, strong industry connections, and a high quality of life.
Question: What are the main skills required for this PhD position?
Applicants should have a solid foundation in signal processing, machine/deep learning, numerical optimization, and Python programming. Experience with graph neural networks is a plus.
Question: How does the INTERACTION project contribute to the field of digital health?
The INTERACTION project aims to develop advanced AI frameworks for integrating multimodal cardiac data, enabling more accurate and personalized risk prediction for ventricular arrhythmias, which can significantly improve patient care.
Question: Where can I find more information about the application process?
Detailed information about the position and application instructions can be found on the LinkedIn post by the project supervisor:
https://www.linkedin.com/posts/ahmad-karfoul-25348648_phd-position-ugcPost-7459710370237251585-DI9d
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