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degree-granting research university with a fully integrated liberal arts and science undergraduate program in the Arts, Sciences, Social Sciences, Humanities, and Engineering. NYU Abu Dhabi, NYU New York
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Description The Center for Smart Engineering Materials (CSEM), NYU Abu Dhabi (NYUAD), is inviting applications for a fully funded Post-Doctoral Associate position in the areas of materials and
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, including electrically conductive membranes. The successful candidate will work under the supervision of Professor Raed Hashaikeh in the Mechanical Engineering Department, collaborating with a
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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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, coding pipelines for optical imaging/photometric data analysis, and spectral modelling, are strongly encouraged to apply. Successful candidates will be encouraged to develop independent projects in
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. The central goal of the laboratory is to study the neural mechanisms involving dynamic RNA modifications during cognitive development and decline. To achieve this, research projects rely on the use of a
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hold a Ph.D. in Mathematics and Applied Mathematics (or be close to completing one). Ph.D. holders with a strong publication record and a background in analysis and PDE are encouraged to apply
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to develop novel enabling technology on hydrodynamic stability analysis that has applications in the study of the development of singularities, the long-time behavior of complex systems, the formation
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Description The Center for Sand Hazards and Opportunities in Resilience, Energy, and Sustainability (SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a
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apply. A PhD dissertation or research papers that demonstrate a strong interest and research focus in any of risk analysis or minimization, robust optimization, deep learning for systems, probabilistic