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Field
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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
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. This project aims to develop novel polymer-based nanoparticles for the non-viral delivery of mRNA vaccines directly to mucosal tissues, such as the respiratory tract. By leveraging high-throughput automated
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) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers , applies to Postdocs, Research Assistants, Research and Teaching Technicians, Teaching Fellows
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per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers, applies to Postdocs, Research Assistants, Research and Teaching
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experimentation via laboratory automation and III) AI navigation of the complex landscape described. This powerful combination not only supports the creation of AMP based nanomedicines, but also addresses a central
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the risk of missed defects. Using the power of Artificial Intelligence (AI), this research aims to: - Automate defect detection in complex 3D structural data - Enhance diagnostic accuracy and processing
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About the ProjectProject details: Next-generation networks are rapidly outscaling the capabilities of traditional management paradigms. While early AI/ML models offered a degree of automation, they
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discovery, and deep-learning algorithms • Neutron scattering and advanced characterisation techniques The successful candidate will work closely with other PhDs and postdocs involved in similar investigations
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and external partners. Benefits: Automation of screening with ML can enable more regular eye screening for patients. This will reduce the undetected cases of retinopathy, enable retinal issues to be
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-efficiency trade-offs, using automated configuration to find Pareto-optimal designs under real deployment constraints. 2) Build the distributed learning loop. Develop the learning and update mechanisms