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environments. Working closely with partners in the NEXPECH₂ project, you will analyze their materials and provide valuable insights to help optimize device performance. Additionally, you will support
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join a consolidated research group at the University of Oviedo. The work will include experimental characterization and numerical modelling of structural materials with applications across various
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innovative experiments. The work will include defining and optimizing in vitro models using diverse cell types and advanced platforms, as well as establishing correlations between applied mechanical stimuli
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environments, specifically Computer Vision, Machine learning algorithms and methods for rock characterization, fragmentation prediction, and mining optimization. Specific Requirements Good academic and
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be clinically tested in a small animal model. (Ref: PID2022-136961NB-I00)(HR25-00225). Main functions: Design, fabrication, and optimization of liquid-walled pumps (Qpump) capable of achieving
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valuable insights to help optimize device performance. Additionally, you will support collaborators by assisting with in-situ TEM measurements, facilitating cutting-edge research in sustainability and energy
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numerical simulation and data processing. Willingness to travel Additional Information Website for additional job details https://www.uji.es/seu/info-adm/tao/convocatoriesRH?pConv=3326&pCategoria=14 Work
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, fluorescence screening, RNA footprinting and computational methods to study the atomic structure and dynamics of the RRE, identify new antiviral candidates and guide the optimization process. The publication
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written and spoken At least 1 year experience in similar fields Additional Knowledge and Professional Experience Experience with revision control systems (e.g., SVN or Git) Experience in numerical air
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Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and