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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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program integrates the study of wireless networking, protocols, sensor networks, security, software development, embedded systems, data analytics, and big data to provide students with the knowledge and
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embedded systems from vulnerabilities rooted in sensor physics, studying the impact of physical signals (e.g., acoustics, lasers, electromagnetic emissions) on AI and sensing systems, and innovating hardware
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, the ML Engineer hired for this position will work with Benjamin Gyori at Khoury College and Ayan Paul at EAI and is expected to develop AI algorithms for LLM based RAG systems, work with graduate students
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Technologies (BEST) Group led by Prof. Ravinder Dahiya. BEST group aims to bring fundamental changes in the way the sensors and electronic devices are developed using materials designed for sustainability, and
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are typically acquired through the completion of a Ph.D. or equivalent. Expertise in robotics, in at least one of robot control, motion planning, integrated sensor-based control, dealing with uncertainty in
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to the bioinformatics field. Courses include primarily biological science courses with minor algorithmic components and primarily programming courses with a focus on bioinformatics methods. Such graduate courses seek
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, and data analysis for human subjects research. · Develop and apply computational models and algorithms to support robotic augmentation studies. · Contribute to manuscript preparation and dissemination
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sensor-based control, dealing with uncertainty in robotic systems, or human-robot interaction. Documents to Submit: Resume and cover letter About Khoury College: We invite you to join a fast-moving
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students to deploy AI systems in up to 200 homes in Atlanta, GA. You will be responsible for designing and deploying the infrastructure that connects sensors, AI inference systems, large foundational models