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as instructor with the rank open, depending on experience. Instructors support NYU Abu Dhabi's educational mission by assisting faculty with courses in Computer Engineering. These courses include among
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machine learning. The successful applicant will participate in research involving human computation, knowledge discovery, machine learning, and data science. The position will provide the opportunity
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Description The Modern Compilers Lab in the Computer Science program at New York University Abu Dhabi, seeks to recruit a research assistant to work on the intersection of compilers and deep
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Description The New York University Abu Dhabi Computational Approaches to Modeling Language (CAMeL) Lab seeks to hire a post-doctoral researcher to work in any of the lab research areas, to be
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Description The New York University Abu Dhabi Computational Approaches to Modeling Language (CAMeL) Lab seeks to hire a new research assistant to work in any of the lab research areas, to be
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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
Geotechnical Engineering, Civil Engineering, or a related field, and should demonstrate strong expertise in at least two of the following areas: Large-deformation numerical modeling (e.g., Coupled Eulerian
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Description The Applied Computational Engineering laboratory in the Division of Engineering, New York University Abu Dhabi, seeks to recruit a Research Associate to work on the numerical modeling
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Experience in the development and coupling of numerical methods for solid mechanics modeling Postdoctoral Associate Employment at NYUAD: The terms of employment include highly competitive salary, medical
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Experience in machine-learning modeling for solid mechanics applications Experience in the development and coupling of numerical methods for solid mechanics modeling Post-Doctoral Associate Employment at NYUAD
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aims to identify biomarkers in the eye and brain that explain vision loss, building on our previously-developed method linking clinical, neural and behavioral data (Allen et al., 2018; Miller et al