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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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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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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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, 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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, applied maths, or scientific computing as well as proficiency in Python/C++. Exposure to ML or automatic differentiation is a plus. You must be curious, collaborative, and motivated to turn methods
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). The Computer Science group is looking for students to work on one of the following projects Distributed Intelligence for Self-Organising Cloud–Edge Infrastructures Carbon-Conscious Resource Scheduling for AI Workloads
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of Warwick. This exciting project, co-supervised by Dr. Sagar Jilka and Dr. Vivek Furtado, focuses on developing a privacy-preserving Artificial Intelligence (AI) tool to predict the worsening of anxiety and
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap