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on model papers using a dedicated micro-tensile machine coupled with 3D real-time in situ observation using synchrotron X-ray microtomography (ESRF). • 3D Image analysis routines will be used to identify
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computational fluid dynamics. • Experience in modeling, uncertainty quantification, or statistical methods. • Experience in data science or machine learning is considered an asset. • Experience with high
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: In-depth mastery of techniques in structural and molecular biology, biochemistry, cell biology, and immunology is essential. Proficiency in using computer tools and software dedicated to data
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spectrum of topics in CNU sections 27 "Computer Science" and 61 "Computer Engineering, Automation and Signal Processing". The laboratory is located at the heart of the Sophia Antipolis technology park, in a
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high-technology group operating in the aerospace, space, and defence sectors. The company designs, manufactures, and supports cutting-edge systems and equipment for civil and military applications. As
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. - Explore the response to controlled forces. Positioning of Magali SUZANNE's team within the MCD unit: We are interested in the process of morphogenesis and seek to understand how organs acquire
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been proposed: traditional computer‑algebra methods [3], reduction of the problem modulo a prime p [6, 2], and symbolic‑numeric methods [4, 1]. This postdoc proposal concerns the second modular approach
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experience in large-scale structure simulations, working knowledge of applications of machine learning techniques in cosmology and/or astrophysics (in particular simulation-based inference), strong programming
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computer, virtual data storage, and access to software tools for specialized data analysis (e.g., Cryo-EM data, crystallography, etc.). Structural microbiology: Exopolysaccharide secretion and host
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missions operated by LATMOS. The postdoc will employ deep learning approaches using satellite data and ground stations. -Understanding the infrared data from the IASI mission and identifying the channels