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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | about 1 month ago
in particular computer vision. Particular topics of interest include visual comprehension, hyperspectral imaging, numerical and parallel optimization, and unsupervised learning. A particular emphasis
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 17 days ago
across diverse biological systems. More broadly, this work is part of the DynaNova program and the DELTA team, which aim to establish a new paradigm for dynamics-aware modeling of macromolecular systems
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a home-build photoacoustic spectrometer (Lucas et al., 2022). To characterize the nanoscale temperature distribution, we propose to combine the pulsed excitation of photoacoustic imaging with a
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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on fatigue management, maintaining daily activities, the role of leisure, and body image. Logbook system: Participants will be asked to document their experiences with changes (dietary and physical) over
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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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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 3 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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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
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heritage, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Sub Nanosecond Imaging