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Field
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areas: Computer Vision and Image Processing Machine Learning, Deep Learning, and Reinforcement Learning Large Language Models (LLMs) and Multimodal Models Generative AI, Agentic AI, Physical AI, and
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knowledge and hands-on experience in: Deep learning frameworks (e.g., PyTorch, TensorFlow) Deep learning models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and
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of distribution networks with DERs, and applications of stochastic programming/approximate dynamic programming approach. Expertise in data analysis for clustering and classification Knowledge in visualization tools
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performance evaluation of computer systems and networks for interactive applications, preferably with AR/VR/XR, robotics, haptics, multimodal or multimedia systems. Directly relevant areas are preferable
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in technical theatre as well as solid organizational and essential computer skills. Experience working with theatrical audio, lighting, and video systems is a plus. Hands-on knowledge of lighting
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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 Technology (KAUST) is
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for equipping modern operating systems with expressive, low-overhead observability mechanisms, enabling the state of the kernel (e.g., processes, network, files) to be queried through a high-level declarative
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Florida International University is a Top 50, preeminent public research university with 55,000 students from all 50 states and more than 140 countries, as well as an alumni network of more than
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engagement platform designed by the Executive Director, and running day-to-day alumni communications. Builds and maintains the operational network of post-fellowship career opportunities, including employer
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conducting user experience studies and quantitative performance evaluation Documented experience in the system performance evaluation of computer systems and networks for interactive applications, preferably