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the Project PI. ii. Meeting the deliverables by carrying out the computational simulations and experimental studies planned for under the scope of work of the projects within the timeframe to meet the
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technical 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
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interdisciplinary team on developing high performance urban farming module via computational and empirical analysis Support in the design and maintenance of the vertical farm to be setup in SIT@Dover and willing
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