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geotechnical applications Data acquisition and processing from monitoring systems Validation of modeling results against experimental and monitoring data Postdoctoral Associate Employment at NYUAD: The terms
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experimental data. Required Qualifications: A successful applicant must have a PhD in Engineering Mechanics, Civil Engineering, or Mechanical Engineering. Applicants are expected to demonstrate research
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, and comparisons to AGN and other compact objects, are also studied. A host of techniques and approaches will be employed, in particular using a wealth of multi-wavelength data of transient XBs
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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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Post-Doctoral Associate in the Division of Social Science - Dr. Morgan Hardy and Dr. Veda Narasimhan
data collection and experimental research designs. The candidate should have a demonstrated overlapping interest in topics related to Morgan Hardy and Veda Narasimhan’s existing research portfolios
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expertise in these areas is highly encouraged. The selected candidate will work on cutting edge technologies in an excellent research environment, with a potential to work with a Quantum Computer through our
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, curriculum vitae, research statement, transcript, and contact information for three referees, all in PDF format. Applications will be reviewed on a rolling basis and considered until the position is filled
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on natural language processing, computational linguistics, and data science. The main lab research areas are Arabic natural language processing (orthography normalization, grammatical error correction
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Information Theory. While excellent candidates with other research interests might be considered, priority will be given to those able to relate to one or more of the above topics. Applicants must have a PhD in
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infrastructure monitoring, as well as connected autonomous vehicles Integrating multi-modal sensor data with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation