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Field
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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-less segmentation and registration workflow, integrating with in-house THR pre-planning to create a complete navigation system, and validating it through cadaver experiments. The proposed work will
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several benefits, including thermal conductivity, electrical insulating and creating the necessary structural integrity needed around the battery. However, this process can be slow, induces an element of
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to ensure that integrity, ethics and excellence are at the core of our research activities and fully embedded in our research culture. View All Vacancies
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meetings. Project Background This project directly supports QCI3's vision of integrated and interconnected implementations by developing essential benchmarking tools that bridge across all three themes
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markers of severe distress, could enable earlier and more accurate prediction of Category 1 emergencies. The integration of such tools into call centre workflows promises to improve decision-making speed
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phenomenon where hydrogen atoms permeate storage materials, compromising their structural integrity. The research will create a multi-layered, self-healing coating that will limit hydrogen adsorption by
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its safe and efficient storage, particularly due to hydrogen embrittlement – a phenomenon where hydrogen atoms permeate storage materials, compromising their structural integrity. The research will
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technical, economic, and social reasons. This leads to the need to integrate several new types of devices both at transmission and distribution level (e.g. renewable generation, HVDC interconnectors, electric
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design by integrating human-centric principles with nature-inspired AI-driven generative design methodologies to achieve absolute sustainability that respects planetary boundaries while fulfilling human