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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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data. Manage preferences for further information and to change your choices. Accept all cookies Reject optional cookies Skip to main content Director, Cancer Prevention & Early Detection Center
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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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requires an unprecedented computing and storage infrastructure. Processing these enormous amounts of data in a future data center, however, consumes significant resources. The fundamental challenge is: How
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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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, “Physics-Informed Super-Resolved Ultrasonic Imaging,” 11th Junior Conference on Data Science and Engineering, 2026 [ Online ]. # Cea-List Methods / Means FMC ultrasonic data; Algorithm unrolling
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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