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-edge advancements in automated drug discovery and bio-instructive material manufacture. The project aims to utilise flower waste as a sustainable feedstock to discover new bioactive small molecules, then
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(SFDI) and also from our custom-built photoplethysmography (PPG) sensor. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection
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