10 postdoctoral-machine-learning Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI
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collection, data engineering, statistical modeling, or computational text and image analysis; Machine learning, natural-language processing, large language models, or evaluation and auditing of AI and online
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considered. General interest in responsible AI, coursework, use of machine-learning tools, unpublished projects, or publications only in unrelated venues do not satisfy this requirement. For collaborative
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Description The Center of Cyber Security, New York University Abu Dhabi, seeks to recruit a postdoctoral associate to work on state-of-art research on security and privacy. The researcher will be
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
and experimental, in the broad field of nonlinear mechanics. Preference will be given to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure
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-controlled subsystems. Hands-on expertise in one or more of the following areas: optical systems, lasers, spectroscopy, quantum optics, spin physics, magnetic resonance, precision measurement, electronics
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure interactions. Applicants must hold a Ph.D. in Mechanical Engineering or a closely related discipline, with
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. Kyriakopoulos seeks to improve the autonomy of Field Robotic systems by fusing control theoretic and machine intelligence approaches. Formal models are directly applied in real experimental facilities. Marine
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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 Experience in digital rock technology