Performance Evaluation of Cell-Free Massive MIMO (CF-mMIMO) over Realistic Propagation Models and Development of GenAI Models for Channel Data Augmentation

Stage M2/PFE/ingénieur Description: This internship will be conducted at IETR – Rennes University, in collaboration with IEMN – Lille University. The goal is to evaluate and enhance the performance of cell-free massive MIMO (CF-mMIMO) systems using advanced signal processing and data-driven techniques. Specifically, the intern leverage signal processing schemes for…

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Self-supervised training strategies for deep hyperspectral unmixing and uncertainty quantification

Stage M2/Ingénieur (+thèse) The full subject, including images and references, can be found at https://partage.imt.fr/index.php/s/4kZRXNwMfaZrscn Context The remote sensing field aims at exploiting satellite or aerial images for earth observation. This, in turns, has an increasingly important social impact, as the applications of these methods include tackling key challenges such…

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Evaluation and Combination of Perception Methods in Robotics

Stage master Research team: RAP, Robotic department — LAAS-CNRS, Toulouse, FranceAdvisors: Maxime Escourrou (maxime.escourrou@laas.fr), Ariane Herbulot (ariane.herbulot@laas.fr)Duration: 4 — 6 monthsSchooling level: MasterStarting date: Flexible around March 2025 Keywords: Perception, Computer Vision, Data fusion, Robustness The aim of this internship is to evaluate the results of several object tracking methods…

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Design of an Unrolled Neural Network for Hyperspectral Pansharpening

Stage M2/Ingénieur Pansharpening is a fundamental and crucial task in remote sensing which generates a high-resolution hyperspectral image by fusing a low-resolution hyperspectral image and a high-resolution panchromatic (or multispectral) image. A range of methods formulate pansharpening as an inverse problem from which iterative algorithms are derived. Those methods depend…

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Face Swapping Temps Réel

Stage M2/Ingénieur L’équipe SAFE du laboratoire GREYC situé à Caen propose un stage de recherche d’une durée de 6 mois sur la génération de deepfake vidéos, sujet d’actualité notamment depuis l’utilisation des techniques d’apprentissage profond (deeplearning) et de GAN inversés. Les étudiants intéressés peuvent envoyer leur candidature à : Christophe…

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Evaluation and Application of Foundation Models for 6D Pose Estimation in Industrial Environments Using Digital Twins

Stage M2 Scientific Fields: Computer Vision, Industry of the Future Keywords: Foundation Models, 6D pose estimation, Digital Twin, Synthetic and Hybrid Dataset, Manufacturing environments Supervision Name Position, Title @ Nicolas Ragot Associate professor nragot@cesi.fr Vincent Havard Associate professor, HDR vhavard@cesi.fr Works Details of the tasks This M2 internship is part…

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Adaptive optics control and learning

Stage M2 Internship supervisors: Eric Thiébaut, Michel Tallon @ : eric.thiebaut@univ-lyon1.fr, mtallon@obs.univ-lyon1.fr Address/Workplace: CRAL – site Charles André : 9 avenue C. André, St Genis Laval Hosting research team: AIRI Internship title: Adaptive optics control and learning Summary of proposed work: Context: Adaptive optics (AO) systems are used by most…

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Direct detection and characterization of exoplanets: statistical learning, multi-epoch and multi-spectral data fusion

Stage M2 Internship supervisors: Olivier Flasseur, Eric Thiébaut, Maud Langlois @ : olivier.flasseur@univ-lyon1.fr, eric.thiebaut@univ-lyon1.fr, maud.langlois@univ-lyon1.fr Address/Workplace: CRAL – site Charles André : 9 avenue C. André, St Genis Laval Hosting research team: AIRI Summary of proposed work: Context: The direct observation of the close environment of stars can reveal the…

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Weakly supervised deep learning for particle detection in fluorescence imaging

Stage M2 Link to the subject: https://images.icube.unistra.fr/img_auth_namespace.php/9/9f/M2_internship_ICube.pdf Context Macromolecular assemblies are large complexes of proteins involved in most cellular processes. The field of structural biology aims to decipher their molecular structure. In particular, the goal is to identify and localize the proteins inside the structure, in order to infer the…

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