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Field
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administrative activities, adapt existing and develop new methodologies in robotics, design working algorithms from theories, deploy and test algorithms on hardware as appropriate, and analyse data from a variety
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streets of Edinburgh. These annotations will provide ground-truth information for machine learning algorithms, contributing to our research and development efforts to develop safe human-centred AI
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sensor and IoT systems deployed on unmanned robots and drones, these algorithms are tailored for rapid response scenarios, such as the release of biological, chemical, or radiological hazardous substances
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sensor and IoT systems deployed on unmanned robots and drones, these algorithms are tailored for rapid response scenarios, such as the release of biological, chemical, or radiological hazardous substances
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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for precise characterisation of nuclear environments through leveraging robotics technologies. This will be achieved by developing a modular swabbing puck equipped with advanced tactile sensors, acquiring
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application, polygraph data capture and analysis has received limited systematic research and does not yet incorporate modern sensors, computing and analytical techniques. Project: This project aims to explore
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statistical and machine learning techniques for dynamic energy system modelling Develop advanced optimization algorithms for building energy management and control (e.g., MPC, RL) Develop and evaluate digital