59 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions at NEW YORK UNIVERSITY ABU DHABI
Sort by
Refine Your Search
-
at the intersection of artificial intelligence and cultural heritage. The successful candidate will be involved in cutting-edge research and development in 3D computer vision and machine learning for the digital
-
University Abu Dhabi invites applicants to apply for the open Post-doctoral Associate position to perform primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control
-
Science, or Information Science, or a bachelor’s degree with several years of experience and expertise in their field. The position requires experience with at least one of the following: Data Science, Machine Learning
-
Science, or Information Science. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data
-
-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
-
exploring new modes of human-computer interactions. Has demonstrated experience in exhibiting works and/or presenting at festivals and conferences, with the ability to teach and support students in developing
-
topics: Free space optical communication Visible light communication DSP for coherent optical communication Machine learning and AI-native physical layer design Optical reconfigurable intelligent surfaces
-
Engineering to educate the next generation of global leaders. Multidisciplinary research and exceptional teaching in a highly diverse and inclusive campus community are hallmarks of the University’s mission
-
emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
-
networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical learning theory and complexity analysis