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. In this role, you will collaborate with leading experts in autism research, computer vision, machine learning, psychiatry, and developmental science to develop innovative technologies that improve
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King Abdullah University of Science and Technology | Saudi Arabia, | Saudi Arabia | about 17 hours ago
02, 2021 Description The Computer Vision-Core Artificial Intelligence Research ( Vision-CAIR ) group led by Prof. Mohamed Elhoseiny at the CS Program of the King Abdullah University of Science and
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interdisciplinary professional partners. Other duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong
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. Interacts with interdisciplinary professional partners. Other duties as assigned. REQUIREMENTS: REQUIRED: Master of Science (MS) in computer vision, machine learning, artificial intelligence, or a
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. Interacts with interdisciplinary professional partners. Other duties as assigned. REQUIREMENTS: REQUIRED: Master of Science (MS) in computer vision, machine learning, artificial intelligence, or a
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interdisciplinary professional partners. Other duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong
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large-scale models Expertise in at least one of computer vision, speech recognition, or multimodal learning, with experience in real-world technology deployment Experience with human-in-the-loop
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tasks operating technically advanced and complex alarm computer systems, video surveillance systems, and mobile radio systems. They use a computer-aided dispatch system (CAD) to input, retrieve and
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top-tier venues and will have dedicated time for advancing their own research. The project is in collaboration with CCL (The Center for Connected Learning and Computer-based Modeling, PI Uri Wilensky
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., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision, tracking, spatio-temporal modeling, edge learning