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performance and/or neural structure/function that govern the rate of cognitive development in childhood and/or old age. The post holder will have an interest in theory-based developmental cognitive (neuro
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fabrication by sputtering (e.g., magnetron sputtering; thin-film deposition workflows). • Materials characterization methods (morphology, structure, composition), e.g., SEM/EDS, XRD, profilometry, IBA, etc
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-of-the-art theory and modeling. For that purpose, CEA/IRIG is actively developing the “TB_Sim” code. TB_Sim is able to describe very realistic qubit structures down to the atomic scale if needed using
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consortium, translating complex CFD results into actionable insights for structural and control engineers. Where to apply Website https://www.academictransfer.com/en/jobs/359872/postdoctoral-researcher-high-fi
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 9 days ago
of existing theoretical frameworks in solid‑state physics, electronic structure, lattice dynamics, and quantum transport, or development of new theoretical approaches in these fields, particularly for novel
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Assistant Professor (postdoctoral researcher) in the research employee group at the Department of Road Construction Technology. The successful candidate will conduct research within an international research
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of the project MESSENGER: « Can rock-powered Microbial EcoSyStEms provide valuable iNsiGhts into early life and its emERgence? » funded by the PEPR ORIGINS (https://pepr-origins.fr/en/ ) and integration
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. The Nienhaus Research Group is interested in understanding the role of nanoscale structure on the ensemble materials properties. The group has pioneered solid-state perovskite-sensitized photon upconversion
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Code76821Street1 rue TesnièreGeofield Contact City Mont-Saint-Aignan Cedex Website https://www.univ-rouen.fr/etablissement/structures-de-formation-et-de-recherche/unites-de-recherche-et-de-service/carmen-institut-de
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). The position will focus on using AI, particularly multimodal transformer-based deep learning models, to extract and leverage information from genomic and chemical structures. Applications include infectious