DESCRIPTION :
* Literature Review: Conduct a comprehensive review of current research in federated learning, specifically focusing on its applications in healthcare. Identify gaps in existing studies.
* Algorithm Development: Design and implement novel federated learning algorithms tailored for healthcare data, addressing challenges such as data heterogeneity, privacy concerns, and communication efficiency.
* Data Management: Collaborate with healthcare institutions to acquire and manage diverse healthcare datasets while ensuring compliance with data protection regulations.
* Mentorship: Supervise graduate students or interns working on related projects, providing guidance in research techniques and methodologies.
Principales activités
* Data Integration: Develop methodologies for coherent data analysis and standardization from heterogeneous medical information including medical imaging and text records. This will involve:
* Organizing and mapping data expressed in natural language within textual documents.
* Creating automated, scalable Extract, Transform, Load (ETL) procedures utilizing Large Language Models (LLMs) suitable for federated environments.
* Developing innovative methods for federated analytics, allowing for statistics and visualization of complex multi-centric data.
* Continual Learning: Operationalize continual learning practices within a federated learning context. Responsibilities will include:
* Defining novel metrics for quantifying the quality of federated models in production.
* Investigating optimal methods to assess and account for clients' heterogeneity and data changes in a federated setting.
* Exploring federated unlearning mechanisms to ensure compliance with GDPR and uphold the "right to be forgotten."
* Collaboration: Work closely with a multidisciplinary team of researchers, healthcare professionals, and technology developers to ensure the applicability and effectiveness of the developed methodologies. Supervision of interns and students, and presentation of the results in relevant scientific and clinical venues.
Code d'emploi : Chargé de Recherches (h/f)
Domaine professionnel actuel : Scientifiques
Niveau de formation : Bac+8
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : Intelligence Artificielle, Conception Algorithme, Analyse des Données, Informatique de la Santé, Intégration de Données, ETL, Visualisation de Données, Python (Langage de Programmation), Machine Learning, Fast Healthcare Interoperability Resources, Large Language Models, Technologies Informatiques, Health Level Seven International, Gestion des Données, Programming Languages, Sens de la Communication, Pensée Critique, Éthique, Résolution de Problèmes, Enthousiasme, Sens de l'Organisation, Esprit d'Équipe, Innovation, Recherche, Systèmes Automatisés, Génie Biomédical, Imagerie Médicale, Dossiers Médicaux, Travaux Cliniques, Conformité Réglementaire, Organisation d'Événements, Soins de Santé, Revue (Publication Périodique), Approche Pluridisciplinaire, Opérationalisation, Recherche Post-Doctorale, Visualisation, Standardisation, Etudes et Statistiques, Workflows, Métrique, Science des Données, Littérature, Protection des Données, Coaching, RGPD, Publication / Edition
Courriel :
Marco.Lorenzi@inria.fr
Téléphone :
0139635511
Type d'annonceur : Employeur direct