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Home   /   Post Doctorat   /   Unsupervised Few-Shot Detection of Signal Anomalies

Unsupervised Few-Shot Detection of Signal Anomalies

Artificial intelligence & Data intelligence Engineering sciences Mathematics - Numerical analysis - Simulation Technological challenges


Our laboratory, located at Digiteo in CEA Saclay, is looking for a postdoc candidate working on the subject of anomaly detection in manufacturing processes, for a duration of 18 months starting from Feburary 2022. This postdoc is part of HIASCI (Hybridation des IA et de la Simulation pour le Contrôle Industriel), a CEA LIST project in an internal collaboration which aims at building a platform of AI methods and tools for manufacturing applications, ranging from quality control to process monitoring. Our laboratory contributes to HIASCI by developping efficient methods of anomaly detection in acoustic or vibrational signals, operating with small amounts of training data. In this context, the detection of signal anomalies (DSA) consists of extracting from data the information about the physical process of manufacturing, which is in general too complex to be fully understood. Moreover, real data of abnormal states are relatively scarce and often expensive to collect. For these reasons we privilege a data-driven approach under the framework of Few-Shot Learning (FSL).


Département Métrologie Instrumentation et Information (LIST)
Service Intelligence des Données
Laboratoire Intelligence Artificielle et Apprentissage Automatique
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