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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology
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) positions to join our team. These full-time positions offer a unique opportunity to engage in cutting-edge interdisciplinary research focused on Generative AI within the context of construction robotics
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thermal and/or thermochemical energy storage systems. Implementing and validating advanced thermodynamic models for performance prediction and optimization. Collaborating with experimentalists and industry
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Citizenship or Permanent Residency required Preferred Additional Qualifications • Background in photogrammetry, 3D graphics, or sensor fusion for structural modeling • Track record of publications in top venues
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, advanced characterisation and materials modelling. The ambition for this project is to carry out multidisciplinary research that will explore Li-rich three-dimensional cathodes free from Co and Ni and with
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Discrete Element Method (DEM) models to simulate the charging and formation of multi-component, poly-disperse burden mixtures in the blast furnace. The goal is to optimize burden structure and permeability
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biopolymer structure/functional properties with advanced biofabrication techniques to develop three-dimensional tissue models, by combining physiological architecture, physical stimuli, and native
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modeling approach for identifying community & policy-level interventions to address the impact of structural racism and discrimination on adolescent substance use and mental health. In this role
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. Recent work includes the discovery and structural characterization of novel dsDNA sensors and the identification of small molecules targeting key innate immune components https://doi.org/10.1016/j.cell
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learning architectures including generative models, particularly for sequence or structural data (e.g. transformers, graph neural networks) Proved experience in working independently and as part of a