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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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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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related disciplines Quantitative imaging, data analysis, or computer vision Numerical modeling of biological systems or continuum mechanics Machine learning/AI, particularly explainable AI (XAI) Hands
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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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of Machine Vision for Institute of Automation and Computer Science Where to apply Website https://vutbr.jobs.cz/detail-pozice?r=detail&id=2000767113&impressionId=25d643a… Requirements Research FieldComputer
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computer vision models for forest-based 3D point cloud data. In recent years, large advances have been made for deep learning algorithms for high-resolution point clouds from small geographic areas. We seek
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detailed data about forest ecosystems. To convert the captured data into meaningful information about the forest environment we seek a PhD candidate who wishes to advance state-of-the-art computer vision
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for Psycholinguistics (Prof. Aslı Özyürek, Dr Esam Ghaleb) is a registered partner. Applicants interested in the topics below may list Prof. Özyürek as a preferred supervisor under MP-AIX. This involves studying how
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contribute to the design and development of Machine Vision approaches for the quantitative analysis and phenotyping of agricultural systems: Training/Development of computational models for the quantitative