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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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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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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and
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biological materials. The development of novel computer-vision-based techniques for contactless detection, quantification, and prevention of sport injury. The development of robotic humanoid simulator and
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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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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
of next generation agentic AI systems. In this PhD programme, you will redefine how the world works, learns, and discovers, turning bold ideas into tools used by millions. You will then become one
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, visions and concerns in innovation. Entry requirements The standard minimum entry requirement is 2:1 in Geography, Sociology, Political Science, Anthropology, Design, Architecture, Engineering, Computer
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Students Project Description The NetZero Futures (NZF) Doctoral Landscape Award is a fully funded EPSRC studentship with the Royal College of Art. The strategic vision of NZF unites RCA-wide Art and Design
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PhD programme focused solely on the safety of artificial intelligence (AI). Our vision is to train future leaders with the research expertise and skills to ensure that the benefits of AI systems
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in AI. Previous publication record in relevant fields: AI, machine learning, computer vision, etc. Previous successful project on a relevant topic. Good knowledge of statistics, probability