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underlying mental processing speed. The successful applicant will drive a fascinating project that links processing speed measures to previously acquired functional and structural MRI data. The project will
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
interested in candidates with a strong mathematical background and expertise in one or more of the following areas: High-dimensional probability and concentration/functional inequalities Markov processes and
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structure of Turkish. The project will explore both the grammar and processing of long-distance dependencies in Turkish (among other phenomena). The successful applicant will work with the PI to design the
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experience designing or redesigning courses, assignments, and assessments in collaboration with faculty Strong understanding of learning sciences, including motivation, cognitive processes, and development
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of novel nanomaterials with sustainable membranes for desalination and water purification via separation processes. The candidate will work in a multidisciplinary environment consisting of PhD-level
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of research interests, transcript and contact information for referees, who may be contacted through the selection process. The research statement should outline the specific project(s) that the candidate would
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-class research facilities. The preferred starting date is Fall 2026, but earlier dates are also possible based on the excellence of the candidate and the processing steps. Review of applications will
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starting date is September 2026, but earlier dates are also possible based on the excellence of the candidate and the processing steps. Review of applications will start soon after job posting, and will
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Description The Center for Interdisciplinary Data Science and AI (CIDSAI) at NYU Abu Dhabi is a cross‑disciplinary hub where mathematicians, computer scientists, engineers, and social scientists
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hub where mathematicians, computer scientists, engineers, and social scientists join forces to push the boundaries of data‑driven discovery. Our mission is two‑fold: to advance fundamental theory in