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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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allowance. Generous travel, equipment, and publication funds. Access to NYUAD’s world-class research facilities, including a high-performance computing (HPC) cluster with ~30,000 cores and 34 GPU nodes. Start
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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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articulation of instruments. These factors affect precision, efficiency, and the ability to perform complex procedures. Traditional laparoscopic tools often lack tactile sensing mechanisms, are rigid, and have
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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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identified as having uniquely high thermal tolerance from prior research, the candidate will focus on enhancing coral survival, growth and condition (health). The candidate will lead lab and field based
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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