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project TARGET-AI will bring together expertise from multiple research groups to advance the state-of-the-art in combining the most advanced techniques from deep learning/AI with rigorous statistical
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students and senior researchers from multiple disciplines to tackle challenges in sustainable aluminium through AI-driven microstructural analysis. The NEST-WISE project offers a vibrant collaborative
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of the ECHO-EMG research initiative, funded by the Independent Research Fund Denmark (DFF). The project aims to develop a novel system that combines high-density surface electromyography (HD-sEMG) and
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techniques to combine data from multiple sources, integrating both visual and textual information to deepen our understanding of patient health. This studentship offers a unique opportunity to contribute
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often fail to preserve the fidelity of combined datasets, leading to loss of crucial information. This proposal aligns directly with the CAMS Data Analytics Theme and the Grand Challenge of using machine
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neuroimaging techniques. You will conduct both neuroimaging and neural network modelling studies to determine whether and how combining information across multiple sensory modalities (vision and touch) may
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focus on how interactions between species at different trophic levels shape these responses. The project combines (1) analysis of long-term datasets to quantify historical changes in the distributions
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certain class of cyclic aromatic compounds that have been first proposed by Heilbronner in 1954, but still are elusive till date. By combining the expertise of synthesis, matrix isolation, on surface
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implement innovative solutions to enable seamless operation in connected and autonomous mobility use cases. - Validate research outcomes through a combination of high-fidelity simulations and
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these institutional arrangements influence public space outcomes such as accessibility, safety, social value, and sustainability. Key questions include: How can governance structures better coordinate the multiple