Type annonces : Proposition de poste
[StageM2] Predicting water quality in watersheds and rivers using deep learning methods
Laboratory: IMT Nord Europe, CERI Systèmes Numériques, Lille, France Duration: 6 months Supervisors: Christelle Garnier and Anne Savard Context: The water quality in watersheds and rivers is a crucial issue for the environment, human health, and economic development. The main sources of water pollution are varied: agricultural activities, industrial discharges,…
[StageM2] Machine learning-based anomaly detection for environmental time series
Laboratory: IMT Nord Europe, CERI Systèmes Numériques, Lille, France Duration: 5 months Supervisors: Christelle Garnier and Anne Savard Context: Air pollution monitoring, along with various environmental time-series measurements such as heat waves, rainfall or snowfall, noise and humidity, plays critical role in evaluating trends and impact on human well-being worldwide….
[StageM2] Analysis of imaging statistical performance for synthetic-aperture observation systems
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…
[PhD] Détection de signaux faibles en contexte volcanique tropical : Apprentissage profond et traitement avancé du bruit électromagnétique
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…
[StageM2] Self-Supervised Learning for Epilepsy Detection Under Limited Data
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…
[StagePFE/M2] Etude comparative de méthodes d’apprentissage automatique et d’un estimateur classique : application aux modèles de backscatter du fond
[StageM2] Internship in Quantification for Radio-Astronomy
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…
[StageM2] « Navigation résiliente sur terrains précaires avec un Ballbot »
Sujet de stage : « Navigation résiliente sur terrains précaires avec un Ballbot« Résumé : Cette proposition de stage décrit un projet de recherche visant à doter un ballbot de la capacité de surmonter les obstacles qu’il pourrait rencontrer lors de sa navigation. L’objectif de ce travail est de définir les actions…
[StageM2+PhD] Hyperspectral Foundation Models
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…
[StageM2] Predicting water quality in watersheds and rivers using deep learning methods
Laboratory: IMT Nord Europe, CERI Systèmes Numériques, Lille, France Duration: 6 months Supervisors: Christelle Garnier and Anne Savard Context: The water quality in watersheds and rivers is a crucial issue for the environment, human health, and economic development. The main sources of water pollution are varied: agricultural activities, industrial discharges,…
[StageM2] Machine learning-based anomaly detection for environmental time series
Laboratory: IMT Nord Europe, CERI Systèmes Numériques, Lille, France Duration: 5 months Supervisors: Christelle Garnier and Anne Savard Context: Air pollution monitoring, along with various environmental time-series measurements such as heat waves, rainfall or snowfall, noise and humidity, plays critical role in evaluating trends and impact on human well-being worldwide….
[StageM2] Analysis of imaging statistical performance for synthetic-aperture observation systems
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…
[PhD] Détection de signaux faibles en contexte volcanique tropical : Apprentissage profond et traitement avancé du bruit électromagnétique
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…
[StageM2] Self-Supervised Learning for Epilepsy Detection Under Limited Data
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…
[StagePFE/M2] Etude comparative de méthodes d’apprentissage automatique et d’un estimateur classique : application aux modèles de backscatter du fond
[StageM2] Internship in Quantification for Radio-Astronomy
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…
[StageM2] « Navigation résiliente sur terrains précaires avec un Ballbot »
Sujet de stage : « Navigation résiliente sur terrains précaires avec un Ballbot« Résumé : Cette proposition de stage décrit un projet de recherche visant à doter un ballbot de la capacité de surmonter les obstacles qu’il pourrait rencontrer lors de sa navigation. L’objectif de ce travail est de définir les actions…
[StageM2+PhD] Hyperspectral Foundation Models
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…
