Type annonces : Proposition de poste

[StageM2] Analysis of imaging statistical performance for synthetic-aperture observation systems

test

Scientific context Synthetic Aperture Radar (SAR) imaging has become an essential remote-sensing modality to obtain high-resolution images in all-weather, day-and-night conditions (Soumekh, 1999). Modern SAR systems are increasingly used in applications such as Earth observation, surveillance, environmental monitoring, among others. As SAR technology evolves toward higher resolutions and more complex…

Lire la suite

[PhD] Détection de signaux faibles en contexte volcanique tropical : Apprentissage profond et traitement avancé du bruit électromagnétique

test

Mots clés : Estimation robuste du signal MT, deep Learning interprétable, traitement adaptatifen contexte bruité, séries temporelles multivariées, suivi électromagnétique des volcans Résumé : L’exploitation des données magnétotelluriques (MT) en contexte fortement bruité reste un défi scientifique majeur, en particulier pour la détection de signaux faibles associés aux dynamiques profondes…

Lire la suite

[StageM2] Self-Supervised Learning for Epilepsy Detection Under Limited Data

test

Telecom SudParis (IP Paris), France Background Epilepsy is a chronic neurological disease that poses significant challenges to patients and their families, as such, effective detection and prediction of epilepsy can facilitate patient recovery, streamlining healthcare processes [1]. To perform this detection process, machine learning based methods, with recently deep learning…

Lire la suite

[StageM2] Internship in Quantification for Radio-Astronomy

test

Recruit a end of study engineer or Master 2 student in electronics or computer science. The aim of this internship is to explore the potential energy and latency benefits of quantification in radio astronomy imaging. Keywords: quantification, radio astronomy, high-performance computing, energy, optimization Context Radio-interferometers image the sky in radio…

Lire la suite

[StageM2+PhD] Hyperspectral Foundation Models

test

The foundation model (FM) paradigm is undoubtedly a major breakthrough in Machine Learning (ML) for Artificial Intelligence (AI). An FM is a large-scale neural network pre-trained with self-supervision on a vast unannotated dataset and designed to perform downstream tasks with minimal fine-tuning on small annotated datasets. While FMs have made…

Lire la suite

[PostDoc] Deep Anomaly Detection in Time Series

test

Anomaly detection is a challenge in itself. The capacity to detect anomalies is a major ingredient of safe and trustworthy AI systems across major application areas. Anomaly detection is unsupervised by nature, since abnormal events are rare, varied, and cumbersome to collect. Conventional methods can be roughly grouped into three…

Lire la suite

[StageM2] Approche Deep Learning pour l’analyse du mouvement chez des enfants présentant des troubles moteurs et de la coordination

test

Contexte Les troubles moteurs et de la coordination chez l’enfant, tels que les troubles développementaux de la coordination ou certaines atteintes neuromotrices, ont un impact important sur les apprentissages, l’autonomie et la qualité de vie. Leur évaluation clinique repose principalement sur des observations expertes et des tests standardisés, dont l’objectivation…

Lire la suite