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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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; distributionally robust optimization; 2) Graph Neural Networks, Large Language Models (LLMs), and geometric deep learning; and 3) federated learning and privacy preserving computing. Basic Qualifications Candidates
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