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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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[4] M. Josien and R. Prat, Parallel and bias-free RSA algorithm for maximal Poisson-sphere sampling, Computer Physics Communications Moyens / Méthodes / Logiciels RSA, Mérope, ExaNBody Profil du
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algorithm-hardware co-design of adaptive Transformer architectures using ReRAM analog in-memory computing. Using PyTorch and in-house ReRAM characterization data, you will develop reference models and
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addition (arithmetic masking) are commonly used to perform this mixing. The encryption algorithm is then carried out using the encoded data. The application of masking to post-quantum algorithm
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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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, “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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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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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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/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