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garden experiments to test how plant performance is determined by plant genotype, the environment (above- and belowground parasite and warming in different combinations and sequences), and their
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parasite and warming in different combinations and sequences), and their interaction. Plant performance will be assessed by measuring life history traits and fitness and using hyperspectral imaging of both
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-driven shifts in species distributions. Currently, barnacles and other species are manually counted from over 3,000 images each year, which is time-consuming and prone to human error. This project will
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and phenotyping for sustainable crop production with the vision to transform crop production by optimizing breeding and farming management through developing and deploying new technologies. Within
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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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research group in computer vision and machine learning, with seminal results in 3D reconstruction from images, scene understanding, deep learning, optimization, sparsity, etc. IMAGINE is part of
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image description systems tailored for blind and visually impaired (BVI) readers, addressing the diverse and evolving needs of users with different types and levels of visual and cognitive diversities
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databases. Integrating image- and text-derived datasets poses challenges due to differences in scale, structure, and accuracy, requiring robust data fusion and validation. By combining these AI-derived trait
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doctoral student who has Competence and skills: -Strong foundation in computer vision (e.g., object detection, image segmentation) and NLP (e.g., text reasoning, language models). -Knowledge of generative AI
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the center. Together you will pursue different applications and explore different AI methodologies. The PhD candidate will be supervised by Professor Edmund Førland Brekke. About the project About the