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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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within the Institute of Health Informatics. The studentships will commence from Feb 2026. About the project Successful applicants will work with the programme coordinator to identify a project and a
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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proposal addresses our AD[A]PT research vision, or one of two set briefs outlined below. THE AD[A]PT RESEARCH VISION Despite decades of research giving us clear guidance on how built environment
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About the Project Project details: The rapid evolution of battery technologies drives global lithium demand, and the UK’s 2030 vision seeks a competitive, sustainable battery supply chain to fuel
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principles, lab measurement, computer vision and ArcGIS, potential fieldwork and UAV flying training. Person specification Experience and/or enthusiastic interest in one or more of the following areas
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Peter Hamilton, and Professor Ray Yep), and who also share a vision for public engagement of the discipline. The studentship is funded by an endowed fund at the University. The Centre encourages and