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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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/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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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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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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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
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arrays. Applicant Profile You are a final-year Master’s or engineering student specializing in electrical engineering, microelectronics, applied physics or computer science. You are comfortable with
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qualifications master student or equivalent (2nd/3rd engineering school year) in computer science and/or machine learning; some knowledge of the Python programming language; ability to work in a team
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scientific computing. The candidate should have strong Python programming and numerical optimisation skills. Experience with PyTorch, inverse problems, GPU programming or ultrasonic imaging would be an
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for High-Performance Automotive ADAS & Autonomous Driving, Product Folder. [7] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," IEEE Conference on Computer Vision
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could be the subject of a scientific publication. Applicant Profile Programming languages: Python, C/C++ Knowledge of artificial intelligence and the PyTorch framework Experience in using sensors