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datasets across variable illumination, wave states and turbidity, enabling hybrid deep-learning and iterative optimisation solutions with clear lab-to-field transferability. Entry requirements: We
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-of-the-art deep learning algorithms (e.g., CNNs, RNNs) to identify characteristic signatures of early airway disease. This project is designed for real-world impact. Through established clinical and industry
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an increasingly complex development environment. Areas to consider that impact the modelling are: Framework Language Process How wide / how deep i.e. what do we model and why? How much provides a good answer i.e
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