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information about behavioural patterns, but scoring this manually is time consuming. For this reason, machine learning solutions have been developed to automate behavioural prediction [5-12]. DeepLabCut [5] is
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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and multimodal applications. Required knowledge Candidates are expected to have a solid background in machine learning and Natural Language Processing. Research experience in multimodal research is
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challenging for clinicians and pregnant women. Digital health records, advances in big data, machine learning and artificial intelligence methodologies, and novel data visualisation capabilities have opened up
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gaming coaching reporting tools. The recruited PhD student will perform natural language processing and machine learning research into AI-augmented coaching to provide a pre-coaching session AI-based
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through use of genomics and machine learning in a clinical context. 2. Detect healthcare-associated transmission of AMR in real-time and transform outbreak response through use of novel long-read sequencing
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Over the past decades, we have witnessed the emergence and rapid development of deep learning. DL has been successfully deployed in many real-life applications, including face recognition, automatic