General information
Organisation
The French Alternative Energies and Atomic Energy Commission (CEA) is a key player in research, development and innovation in four main areas :
• defence and security,
• nuclear energy (fission and fusion),
• technological research for industry,
• fundamental research in the physical sciences and life sciences.
Drawing on its widely acknowledged expertise, and thanks to its 16000 technicians, engineers, researchers and staff, the CEA actively participates in collaborative projects with a large number of academic and industrial partners.
The CEA is established in ten centers spread throughout France
Reference
SL-DRT-24-0526
Direction
DRT
Thesis topic details
Category
Technological challenges
Thesis topics
Natural language interactions for anomaly detection in mono and multi-variate time series using fondation models and retrieval augmented generation
Contract
Thèse
Job description
Anomaly detection in mono and multi-variate time series highly depends on the context of the task. State-of-the-art approaches rely usually on two main approaches: first extensive data acquisition is sought to train artificial intelligence models such as auto-encoders, able to learn useful latent reprensations able to isolate abnormality from expected system behaviors; a second approach consists in careful features construction based on a combination of expert knowledge and artificial intelligence expert to isolate anomalies from normal behaviors using limited examples. An extensive analysis of the literature shows that anomaly detection refer to an ambiguous definition, because a given pattern in time series could appear as normal or abnormal depending on the application domain and the immediate context within the successive observed data points. Fondation models and retrieval-augmented generation has the potential to substantially modify anomaly detection approaches. The rationale is that domain expert, through natural language interactions, could be able to specify system behavior normality and/or abnormality, and a joint indexing of state-of-the-art literature and time series embedding could guide this domain expert to define a carefully crafted algorithm.
University / doctoral school
Sciences et Technologies de l’Information et de la Communication (STIC)
Paris-Saclay
Thesis topic location
Site
Saclay
Requester
Position start date
01/09/2024
Person to be contacted by the applicant
GOUY-PAILLER Cédric cedric.gouy-pailler@cea.fr
CEA
DRT/DIN//LIIDE
CEA Saclay
Bâtiment 565, PC 192
91 191 Gif-sur-Yvette
01 69 08 41 87
Tutor / Responsible thesis director
GOUY-PAILLER Cédric cedric.gouy-pailler@cea.fr
CEA
DRT/DIN//LIIDE
CEA Saclay
Bâtiment 565, PC 192
91 191 Gif-sur-Yvette
01 69 08 41 87
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