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publication about this benchmark. # Cea List Moyens / Méthodes / Logiciels AI, Deep Neural Network, Computer Vision, Human behavior analysis Profil du candidat Profile - Students in their 4th or 5th year
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/Computer Vision pour analyser les images radioscopiques d'un mélange triphasique. Stage M2 de traitement et d'analyse d'images issues de radioscopies X de mélanges triphasiques. Mise en œuvre de méthodes de
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, signal processing, computer science, applied physics or a related field, preferably with the PhD awarded within the last two years. You have solid experience in machine learning and/or deep learning
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gaussian representation meets perception models for bev segmentation. In : 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. p. 2250-2259. [3] KERBL, Bernhard, KOPANAS
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Intership Agentic Multimodal AI for Understanding and Reasoning over Complex Technical Documents-H/F
- Computer vision skills - Machine learning skills (deep learning, perception models, generative AI…) - Python proficiency in a deep learning framework (especially TensorFlow or PyTorch) Localisation du
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Profil du candidat Currently enrolled in your final year of a Master's program (M2) or an Engineering School (including gap year students). Strong skills in computer vision and machine learning
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as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), 2020. [2] Kerbl, B., Kopanas, G., Leimkuhler, T., Drettakis, G. (2023). 3d gaussian splatting for real
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Laboratory of Vision for Modeling and Localization (LVML) conducts its research in the field of computer vision and artificial intelligence for the perception of intelligent and autonomous systems
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-Computer Interfaces (BCI). The goal is to optimize communication architectures that minimize thermal dissipation, reduce radio interference, and achieve high data rates. This work aims to create more
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workspace learning,” in European Conference on Computer Vision, 2026. [3] A. Brazi, B. Meden, F. M. de Chamisso, S. Bourgeois, and V. Lepetit, “Corr2distrib: Making ambiguous correspondences an ally to