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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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energy vectors (heat, EM fields etc.). Candidates are expected to have previous experience in designing experimental rigs in a laboratory setting. Key Requirements: The candidate needs to have extensive
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post Experience in cricket pitch preparation. (Candidates with minimal cricket experience but with a sound understanding of sports ground maintenance will be considered) Experience of operation of a wide
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instructions and to understand safety instructions. Due to the nature of the work, candidates are expected to have a good level of physical fitness. A welcoming manner, reliability, professionalism, and a high
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part of the in person, front-line Library User Services Team to deliver high quality customer services, supporting students, staff, and visitors to use Library resources and services. Maintain a
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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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cover in a Hall of around 300 students and will help to ensure the smooth running of the hall. Personal Attributes Candidates must display a high level of personal organisational ability, good
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school enjoys a broad and diverse portfolio of research activity, from manufacturing sciences to renewable technologies, systems engineering and applied high energy physics to energy challenges, as
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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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design services and offer more sustainable solutions, into the industry related to the design and understanding of the performance of geogrid soil reinforcement systems. You will have autonomy to lead and