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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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will 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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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
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!), used in future quantum computer or spatial radio astronomy applications This internship provides an opportunity to improve our understanding of reliability physics at cryogenic temperature, and to offer