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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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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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an interest in image analysis and instrumentation. Website for additional job details https://emploi.cnrs.fr/Offres/Doctorant/UMR6251-ANAGAU-002/Default.aspx Work Location(s) Number of offers available1Company
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 18 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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interior and surface (imaging, observations, analyses and modelling in the fields of geophysics, geochemistry and digital science) to environmental and palaeoenvironmental themes, employing experimental
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lining of blood vessels— which plays a central role in the development of vascular diseases. Blood vessels further complicate this picture through their complex geometries, featuring diameter variations
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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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, laboratory work, GIS analysis and programming. Work context The proposed thesis is part of work package 2 of the ANR FaSiRé (Archeo-seismicity) project, led by C. Perrin. The work will involve: - image
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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