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Assist Prof in Next-Generation Approaches of Remote Sensing for Applications to digital soil mapping
networks and deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Experience with data pre-processing, feature extraction, and data
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Assist. Prof in Next-Generation Approaches of Remote Sensing for Applications to Digital Agriculture
networks and deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Experience with data pre-processing, feature extraction, and data
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networks and deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Experience with data pre-processing, feature extraction, and data
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Criteria: Strong programming skills in Python and R and experience with deep learning frameworks such as TensorFlow, Keras, PyTorch, etc. Mastering neural networks and deep learning architectures, including