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anomaly detection using advanced and optimized methods. • Literature review (image processing, deep learning, vision-language models, diffusion models, etc.). • Generative AI for creating reliable models
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of nearly 6000 exoplanets has revealed an extraordinary diversity in planetary systems. Direct imaging —particularly coronagraphy—has emerged as one of the most promising methods for detecting and
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is that standard ML techniques do not perform well when applied to the urban environment (buildings create “no-data” gaps in the fields to be reconstructed; ML methods geared for image reconstructions
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.8b07216 [8] Dahlia Saad, PhD from Ecole Centrale de Lyon, “What happens at the nanometric scale at the liquid/solid interface? Implementation of an Atomic Force Microscope for 3D-AFM imaging of liquid/solid
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. Neural information coding for efficient spikebased image denoising. Technical Report arXiv:2305.11898, arXiv, May 2023a. URL http: //arxiv.org/abs/2305.11898. arXiv:2305.11898 [cs] type: article. A
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methodological development Participate in system integration and deployment Scientific supervision Supervise and support an intern and a research engineer in the development of a first NILM prototype Transfer
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of techniques — organoids, stem cells, omics approaches, and live imaging — to explore principles of cell-fate specification and tissue morphogenesis. We recently moved into newly renovated laboratory
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
Traitement du Signal et des Images, 2023. Douté, S., Forbes, F., Borkowski, S., Meyer, L., Heidmann, S., 2024. Massive analysis of multi-angular images by inverse regression of reflectance models
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. Experience in neurobiology of addiction, animal behavior (self-administration, stereotaxic surgery), and/or molecular techniques (PCR, western blot, immunohistochemistry). Experience in neuronal imaging is a
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methods that will be explored rely on the following techniques: i) Autoencoders / Variational Autoencoders (VAE) to embed high-dimensional inputs (fields, images, mesh-based signals) into low-dimensional