Offre en lien avec l’Action/le Réseau : BigData4Astro/– — –

Laboratoire/Entreprise : DAVID Lab – UVSQ – Versailles
Durée : 6 mois
Contact : mustapha.lebbah@uvsq.fr
Date limite de publication : 2024-01-31

Contexte :
In this research internship, we aim to test the feasibility of a modern neural methodology based on the generative model, which has been successfully applied to text/image processing. The field of video generation technology has seen significant advancements, with modern models capable of producing highly realistic videos [1, 4, 5]. Drawing an analogy to this, studying the life cycle of an aircraft engine can be viewed similarly to creating a video. In this analogy, each frame represents a distinct flight undertaken by the aircraft, during which multiple continuous parameters forming multivariate time series data. Each multivariate time series can be compared to a frame in a video, reflecting the dynamic states of the aircraft engine during the respective flight.

Sujet :
The aim of this research internship is to strengthen collaboration with Safran.

-Study the current state of the art in deep generative model and multivariate time series,
-By sequentially analyzing this collection of parameters flight after flight, akin to stringing together video frames, we can create a detailed and comprehensive depiction of the aircraft engine’s life cycle, allowing for the identification of behavioral patterns, anomalies and providing predictive insights into the engine’s performance and longevity.
-Based on previous studies [2, 3], implement one or more algorithms/architectures. The results obtained during the internship may lead to contributions to open-source software, or even a scientific publication, depending on the intern’s skills and motivation.

Profil du candidat :
End of engineering degree, M1/M2 in data science, statistics, artificial intelligence, or computer science. Excellent understanding of machine learning basics, particularly deep learning models. Excellent programming skills, especially with tensorflow/keras.

Formation et compétences requises :
End of engineering degree, M1/M2 in data science, statistics, artificial intelligence, or computer science. Excellent understanding of machine learning basics, particularly deep learning models. Excellent programming skills, especially with tensorflow/keras.

Adresse d’emploi :
The internship will be in the DAVID Lab at the University of Versailles

Document attaché : 202312150838_DAVID-UVSQ-Research_Internship_GenerativeMTS.pdf