19 machine-learning Postdoctoral positions at NEW YORK UNIVERSITY ABU DHABI in engineering
-
students opportunities to experience varied learning environments and immersion in other cultures at one or more of the numerous study-abroad sites NYU maintains on six continents. NYUAD is committed
-
distributed decision-making. Applicants must have a PhD in Electrical Engineering, Mechanical Engineering, Computer Engineering, Applied Mathematics, Mathematics, or a closely related discipline, and are within
-
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
-
Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
-
-Doctoral Associate to advance cutting-edge research in machine learning (ML). Our lab explores the intersection of artificial intelligence, and human-computer interaction, striving to create technologies
-
will engage in research that includes applied machine learning, systems building, and Internet measurements. Key Responsibilities: Contribute to driving and developing the lab's research directions and
-
-doctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a
-
://sandrasiby.github.io/ . The lab website can be found at https://sites.google.com/nyu.edu/haven-lab The successful candidate will engage in research that includes applied machine learning, systems building, and Internet
-
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
-
for building energy-efficient and robust brain-inspired, autonomous, and cognitive systems and intelligent vision systems, including efficient learning and inference of complex AI/ML algorithms, specialized