Type annonces : Proposition de poste

Subject title: 3D image reconstruction dedicated to tomographic diffractive microscopy for unlabeled samples.Host laboratory: Laboratoire Hubert Curien (LaHC), 18 Rue Pr B. Lauras, 42000 SAINT-´ETIENNE.Supervisor and contact: Fabien Momey Casella (fabien.momey@univ-st-etienne.fr).Keywords: 3D (tomographic) image reconstruction, numerical simulation, scientific programming with Matlab®,tomographic diffractive microscopy, biomedical applications.Duration: 6 months.Starting date: february/march 2025.Salary:…

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Stage de Master M2 au CRAN, Nancy (03-08/2024): apprentissage profond pour la classification de potentiels d’action neuronaux. Les potentiels d’action, ou spikes en anglais, constituent la base de la communication neuronale, permettant la transmission d’informations au sein des réseaux cérébraux. Ces activités électriques, générées par des neurones individuels, peuvent être…

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Nous recherchons un(e) étudiant(e) en M2 pour un stage au sein de notre équipe de recherche de l’IPBS Toulouse, CNRS UMR 5089, spécialisée dans l’étude du microenvironnement des tumeurs mammaires. Nous travaillons principalement sur des techniques d’imagerie par spectrométrie de masse (MSI) pour l’étude des petites molécules et des lipides…

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The aim of this internship is investigate the end-to-end design of a camera with a large field of view (FOV) and a neural network. This joint design leverages on a differentiable optical model based on ray tracing that can be included in the optimization framework of deep learning methods. This…

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Geometric deep learning has emerged in the fields of computer graphics and computer vision, enabling deep learning models to operate on geometric data such as graphs, meshes, manifolds, and point clouds. Some notable models in this area include Graph Convolutional Networks (GCNs), PointNet, Geodesic Neural Networks (GNNs), and specialized architectures…

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Trained on a large-scale dataset, similar to other vision foundation models, Segment Anything Model (SAM) can generate fine-grained masks given manually defined visual prompts. Despite its remarkable success, however, it does not easily generalize to the segmentation of flexible objects like garments, since unlike the majority of objects, garments depict…

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Geometric deep learning has emerged in the fields of computer graphics and computer vision, enabling deep learning models to operate on geometric data such as graphs, meshes, manifolds, and point clouds. Some notable models in this area include Graph Convolutional Networks (GCNs), PointNet, Geodesic Neural Networks (GNNs), and specialized architectures…

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Stage M Context and Motivations: Text generation using Large Language Models (LLMs) has become a powerful tool in natural language processing, leveraging vast datasets to produce human-like text across various domains. LLMs like GPT-3, LLaMA, and BLOOM have been trained on enormous corpora of text data, enabling them to generate…

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