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The detection of out-of-distribution (OoD) samples is crucial for deploying deep learning (DL) models in real-world scenarios. OoD samples pose a challenge to DL models as they are not represented
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Fully Quantized Vision Transformers: Integer-Only Architecture for Object Detection The French Alternative Energies and Atomic Energy Commission (CEA) is a leading research and innovation
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Domaine Sciences pour l'ingénieur Contrat Stage Intitulé de l'offre Internship (6 months) in Electromagnetics/AI H/F Sujet de stage Backscattering Electromagnetics for Intelligent Object Recognition
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Objectives • Study the SOA and approaches on LLMs/VLMs + reasoning, with emphasis on Ontologies and other formal Knowledge Representation approaches. • Research on the ways the interaction can be exploited and
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privacy attacks [5] and inspire novel privacy-preserving strategies [6,7]. Objectives The goal of this internship is to explore the dual role of diffusion models in attacking and defending Federated
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manifolds relevant to RF detection. The goal is to produce a validated simulation framework that captures the full time‑dependent response of the atomic ensemble to nanosecond‑scale RF pulses and quantifies
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focuses on the analytical synthesis of broadband and dual-band matching networks and power combiners for 6G radar applications. The objective is to develop a component library for integration
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] Lacombe, G., Feliot, D., Boespflug, E. et al. Combining static analysis and dynamic symbolic execution in a toolchain to detect fault injection vulnerabilities. J Cryptogr Eng 14, 147–164 (2024). https