3 offres formation trouvées

Concevoir et améliorer des dashboards et outils de pilotage de l'activitéDévelopper des modèles prédictifsIntégrer des solutions basées sur LLM/RAG L'ISCOD, spécialiste de la formation en Digital Learning, recherche pour une ESN, un Data Scientist (H/F) en alternance pour préparer l'une de nos formations diplômantes reconnues par l'Etat de niveau 5 à niveau 7 (Bac+2, Bachelor/Bac+3 ou Mastère/Bac+5). Choisissez l'alternance nouvelle génération avec l'ISCOD !

Joining LINEACT at CESI for a research internship would be a fantastic opportunity to contribute to innovative projects while deepening my skills in a cutting-edge environment Abstract The study explores the development and utilization of digital twins and extended reality tools to enhance remote maintenance procedures, focusing on creating a functional digital twin and developing an interface for device control in extended reality. Keywords : Extended Reality, Digital twin The technological advancements of Industry 4.0 and the human-centric approaches of Industry 5.0 have paved the way for the implementation of new methodologies and technologies, including digital twins (DT) and extended reality (XR) tools. However, the increasing complexity of installations, combined with the need for external expertise during maintenance operations, calls for a rethinking of remote assistance methods. To address these challenges, our work focuses on utilizing these technologies to improve...

Scientific fields: Artificial Intelligence, Machine Learning, Feature Selection, Digital Health. Keywords: Feature Selection, Multimodal Data, Real-Time, Monitoring, Fall-detection, Health. Internship Topic Stress and emotion monitoring systems play a crucial role in mental health management, especially given the global rise in psychological disorders. Wearable devices equipped with sensors such as ECG (electrocardiogram), PPG (photoplethysmogram), EDA (electrodermal activity), and motion sensors provide a rich source of data for stress and emotion analysis. However, the high dimensionality and noise present in these multimodal datasets pose challenges for efficient processing and interpretability. Feature selection is essential for building robust and interpretable models. Recent advancements in explainable AI (XAI) emphasize the need to ensure that selected features provide understandable information about emotional and stress states, which strengthens trust and adoption by...