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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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combining physical models, sensor data, computational methods, and damage and fracture mechanics concepts to create a virtual replica of the composite tank, enabling predictive maintenance, lifetime
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Computational background: engineering, physics, maths, or computer science. How to apply: Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and a 300-word statement explaining your motivation
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architected materials or metamaterials (MTM) that can undergo targeted non-linear response. You will develop a computational framework that can reveal novel Multiphysics (thermo-mechanical) MTM solutions
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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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curriculum vitae (CV), transcripts and 300-word statement explaining your motivation for applying to this PhD Studentship to: Supervisor Review Form . Our supervisors will perform a comprehensive review to
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300-word statement explaining your motivation for applying to this PhD Studentship to: Supervisor Review Form . Our supervisors will perform a comprehensive review to long-list candidates. Deadline: 8
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combination of experimental work, rig development, and computational modelling, and will equip the student with a unique skill set highly relevant to both academic research and industrial careers such as
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One fully funded, full-time PhD position to work with Alessandro Suglia in the Embodied, Situated, and Grounded Intelligence (ESGI) group at the School of Informatics, University of Edinburgh
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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