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Description The Center for Interdisciplinary Data Science and Artificial Intelligence (CIDSAI) at NYU Abu Dhabi seeks to recruit a highly motivated researcher to work on topics in the theoretical
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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
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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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, 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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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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, 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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Post-Doctoral Associate in Sand Hazards and Opportunities for Resilience, Energy, and Sustainability
). Probabilistic and reliability-based analysis applied to underground structures. Advanced subsurface characterization techniques integrating geotechnical and geophysical data. Geohazard mapping and modeling
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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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healthcare applications. We use large real-world complex datasets, including data extracted from electronic health records and medical images, for applications pertaining to patient diagnostics and prognostics
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areas: Setting the immersive virtual reality experiment, building the choice experiment, managing and running the field experiment, analyzing the data and estimating users’ preferences for Automated Taxis