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magnesium oxide cements (RMC) and Engineered Magnesia Composites (EMC) as part of a broader effort to create sustainable and high-performance construction materials. Building on recent insights
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advanced instrumentation and computational support for high-throughput data collection, visualization, and analysis. The NYUAD-CGSB operates in partnership with its sister center, NYU Biology’s CGSB, in New
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the area of computational heat transfer and machine learning for radiative transfer in scattering media. The successful applicant will use machine learning for solar photovoltaic (PV) and concentrated solar
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advanced control and learning algorithms, particularly in the context of human-robot interaction, in addition to an experience in working with real robotic manipulators. Demonstrate a high degree of self
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of this fascinating project: To design and assess the performance of self-sensing cementitious materials endowed with high sensitivity, resistance to temperature, and durability under the highly basic environment
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functions, supporting faculty and researchers with high-level expertise in automation, robotics, and artificial intelligence. This role combines scientific, technical, and strategic planning duties, providing
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characterization of materials for energy harvesting and construction materials. The center has access to state-of-the-art computational facilities for high-performance computing, in addition to collaboration
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expected to have programming experience with MATLAB, python, shell scripting, and UNIX/LINUX environments. Familiarity working within a high-performance computing environment is ideal. To be considered