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biochemical datasets. A major focus will be on introducing new AI models to chart the chemical “dark matter” of the mammalian metabolome; examples of such models include large supervised or self-supervised AI
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Technologies, ORCID: https://orcid.org/my-orcid?orcid=0000-0001-7930-1751 Topic Description: Smart textile technologies are rapidly advancing, making the formation of conductive tracks directly on textile
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materials manufacturing methods - Experience with metallographic specimen preparation and electron microscopy - Experience with computational modelling, either related to materials behaviour or to X-ray
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atomistic tight-binding and multi-bands k.p models for the electronic structure of the materials. Using TB_Sim, CEA has made significant progress in the understanding of various aspects of the physics of spin
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and Failure of the Surface-Stress "Core-Shell" Model in Brookite Titania Nanorods. Chemistry of Materials, 2020. 32(1): p. 286-298. Ab initio theoretical modeling; Active nanodevices; Atomic scale
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DEPARTMENT: Chemistry and Materials Science/ School of Sciences ACADEMIC DISCIPLINE: Chemistry and Material science POSITION TITLE: Open rank faculty appointment LOCATION: Suzhou, Jiangsu, China
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(SHM), physics-based modeling, and data-driven analytics to enable predictive, performance-based decision-making and improve infrastructure safety, resilience, and lifecycle performance. The candidate is
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: Computational, Quantitative, and Predictive Modeling of Root Systems. This position emphasizes integration of phenomics and other -omics data into predictive frameworks. Research areas may include: Structural
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-edge research in quantum science and engineering, enabling fabrication of electronic and photonic devices across a wide range of materials, including silicon, IIIâ“V semiconductors, diamond, two
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The Rosen Research Group at Princeton University (https://rosen.cbe.princeton.edu) is searching for a postdoctoral or more senior researcher interested in computational materials design and