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, the supervision team have obtained data access to indoor environment sensor data at national scale from a leading industrial collaborator. To pair with this big dataset, outdoor environment data at MetOffice can be
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architectures, and demonstration of showcase applications, like light emitters, light sensors, supercapacitors, and batteries. Research and training tasks will be carried out by a collaborative and
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systems, complex networks, data-driven modeling, machine learning, optimization, and sensor fusion. The division has extensive collaborations both with industry and other research groups around the
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, physiology, psychophysiology, engineering, data science, and cutting-edge sensor technologies. The cluster builds on the success of the Peter Harrison Centre for Disability Sport (PHC) and brings together
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with a wide network of stakeholders, and explore new avenues for medical applications. For ongoing work and publications on this project, please see our website: www.cnnp-lab.com . This is a 12-months
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or sensor arrays. Experience generating, processing and analysing large material property datasets including correlating between multiple techniques, or developing computational reconstruction techniques
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monitoring. These applications rely on remote sensors to capture PCs and wirelessly transmit them to edge servers for downstream tasks, such as registration, i.e., aligning multiple PCs within the same 3D
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applications Multimodal data integration and prediction, including clinical, medical imaging, and sensor data (e.g. ECGs) The PhD student will have the opportunity to conduct both methodological and applied
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temperature and humidity data in cold chains by commercial sensors, and deploy them in end-to-end virtual supply chains. This project also aims to better understand the tradeoffs related to cooling technology
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of diverse, deformable textiles at cycle times below one second, while hyperspectral, NIR, Raman, and RGB sensors feed an edge-compute AI pipeline for real-time decision making that routes each item