Who are we ?
Volta Medical is an innovative, data-inspired US and European-based startup aimed at creating and bringing to the market cutting-edge AI software products to revolutionize interventional cardiac electrophysiology.
Volta has created a data collection and enrichment ecosystem for curated EP lab data which is fed into a physician validated, machine learning methodology to support complex AF solutions. Our newest technology, Volta AF-Xplorer, is a data-driven AI decision support system that easily integrates into the standard physician workflow, providing a patient-tailored approach to complex AF and AT procedures.
Our dynamic, innovative team is made up of 100+ collaborators in the EU and USA from a wide variety of backgrounds including engineers, business professionals and physicians, working together to develop AI solutions that help electrophysiologists help patients.
Interested in joining us in this mission? We are looking for a Machine Learning intern - Graph Neural Networks (GNN) to join our Data Scientist team in Marseille.
Our values
Be a pioneer: Be brave. Don’t be scared of researching, exploring, trying and sometimes failing.Improve patients’ lives: Create products to provide electrophysiologists with the best user-experience possible, to help them improve the lives of patients.Strive for excellence: Push yourselves to deliver the highest quality in everything you do. Learn from your mistakes and aim for the best.Collaborate as a team: Welcome to a multidisciplinary and a diversified team ! Try to understand people from different cultures and environments. Confront your ideas and have fun !Missions
Context Identifying abnormal cardiac electrograms and ablation targets is a critical step in treating heart pathologies such as atrial fibrillation and ventricular tachycardia. Current approaches often rely on traditional methods of signal analysis that can be limited in their ability to generalize across diverse datasets and patient populations. Graph-based machine learning approaches, including Graph Neural Networks (GNNs), are promising for capturing complex relationships and dependencies in these data. Goal The primary objective of this internship is to explore the potential of graph machine learning techniques for the identification of abnormal signals and ablation targets in the context of heart pathologies. Specifically, the successful candidate will: Investigate and implement graph-based approaches (e.g., GNNs) to model and analyze cardiac electrograms (EGM) data. Compare these methods with traditional and other state-of-the-art machine learning techniques. Validate the results using appropriate performance metrics and analyze their implications for practical applications in electrophysiology. Available Data A large dataset related to atrial fibrillation. This dataset includes annotated electrophysiological signals and catheter locations, 3D patient hearts geometries, and the ablation set performed during the surgery. Additional data augmentation techniques may be employed to address class imbalance or data scarcity issues. Responsibilities of the Intern: Dataset Preparation: Implement pipelines for data preprocessing. Model Development: Train and evaluate graph machine learning models, such as GNNs, and benchmark them against traditional methods. Analysis and Validation: Conduct thorough analyses of model performance, interpret results, and assess clinical relevance. Documentation and Reporting: Prepare technical documentation and contribute to a potential publication in a scientific conference. Profile
You have a background in machine learning or data science and strong mathematical knowledge (Linear Algebra, Statistics...).You have proficiency in Python and relevant libraries (e.g., PyTorch, TensorFlow, scikit-learn, Scipy).You have familiarity with graph theory and GNNs (a plus).You have strong analytical and problem-solving skills.You are interested in biomedical applications and willing to learn domain-specific knowledge.Benefits
You will have the opportunity to work on cutting-edge research in machine learning and healthcareYou will have the potential to contribute to a scientific publicationYou will gain hands-on experience with real-world medical data and applicationsYou will receive mentorship from experienced researchers and domain expertsDuration of the internship: 5-6 months A monthly compensation of 1200 €The information that you provide is subject to personal data processing by Volta Medical as data controller for the purpose of managing your job application.
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