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optimization of machine learning methods for processing Quantum OCT (Q-OCT) signals. Design, training, and testing of neural network architectures for artefact and dispersion removal in Fd-Q-OCT and SS-Q-OCT
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learning, computer vision, or a related field; knowledge of affective computing, generative AI models, and deep-learning methods; proficiency in Python and experience with machine-learning libraries
Searches related to machine learning and image processing
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