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complex nonlinear models, with unique research opportunities in algorithm and hardware co-design, particularly for resource-constrained environments. Applications focus on edge intelligence
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Scaling analog in-memory computing towards large generative AI models H/F Subject Your work will span device-to-algorithm co-design, focusing on state-of-the-art small language model engineering and
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publishing in peer-reviewed scientific journals Your Profile Master’s degree in electrical engineering, physics, computer science, or a related field Strong academic background in embedded AI and in the
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Liège. Your tasks in detail: Develop an event-driven learning algorithm for latent reasoning that sparsely updates network state and parameters in complex reasoning scenarios. Explore hardware
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initially focus on algorithm development and simulation. Starting from reference methods, you will evaluate different strategies and progressively incorporate realistic ReRAM device characteristics
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will involve: - Understanding the various sensors. - Implementing a calibration algorithm using AI-based methods. - Adopting a rigorous scientific approach to validate the relevance of the proposed
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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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| Institut Pasteur, Paris Can we restore hearing and balance by correcting the genetic cause of disease? We are looking for an outstanding and highly motivated PhD candidate to join the RESTORE project
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and prepare your doctoral thesis Your Profile Your qualifications and technical background include: An excellent master’s degree or equivalent in computer science, data science, applied mathematics