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on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems tailored to dynamic, human-centered environments. They may also work with diverse signal modalities, including vision
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blind and low-vision individuals in navigating both outdoor and indoor urban spaces. The project's initial phase will employ machine learning models, computer vision algorithms, and real-time sensory
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initial phase will employ machine learning models, computer vision algorithms, and real-time sensory integration to prototype tools such as SafeCross for safe street crossing and EasyPath for indoor
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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems