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data, such as images, sounds, and tactile information. This project is to embed the intelligence into the robotics system. We expect the robot can conduct the inspection autonomously without human
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of generative models by introducing a training regime inspired by the Thinking, Fast and Slow paradigm. Recently, the use of RL has been shown to significantly improve the performance of LLMs. The goal
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the interpretability of these models can be enhanced to support clinical decision-making. This project will leverage the complementary expertise of both supervisory teams in EEG signal processing, graph deep learning
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. The system will leverage cutting-edge techniques in Natural Language Processing (NLP), Machine Learning (ML), and Multimodal Analysis to conduct adaptive interviews, assess candidate responses, and generate
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on shortlisting. If you consent to take part in the study, the ARRC team will use information from the shortlisting process to understand the impact of different CV formats. The recruiting academic will not be told
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are investigating the effects of CV format on shortlisting. If you consent to take part in the study, the ARRC team will use information from the shortlisting process to understand the impact of different CV formats
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are investigating the effects of CV format on shortlisting. If you consent to take part in the study, the ARRC team will use information from the shortlisting process to understand the impact of different CV formats
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of CV format on shortlisting. If you consent to take part in the study, the ARRC team will use information from the shortlisting process to understand the impact of different CV formats. The recruiting
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use this formalisation to encode our STV algorithm on encrypted ballots. This approach aims to ensure both the correctness and privacy of the tallying process, paving the way for verifiable and secure
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harness advanced techniques such as machine learning, optimization algorithms, and sensitivity analysis to automate and enhance the mode selection process. The result will be a scalable methodology that