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biomaterials, development of wear-testing methods, crown-material processing, mechanical characterisation, surface characterisation, micro-CT and quantitative image analysis. An interdisciplinary and
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biomaterials, development of wear-testing methods, crown-material processing, mechanical characterisation, surface characterisation, micro-CT and quantitative image analysis. An interdisciplinary and
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applications to medical data. The project will combine theoretical work on multiparameter persistence with the design and implementation of algorithms for medical image analysis. The successful candidate will
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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. Proficiency in standard molecular and cell biology techniques, including RT-qPCR, ELISA, immunocytochemistry, and Western blotting. Experience with sample preparation, imaging, and image analysis using confocal
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on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured
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. The Department covers a wide breadth of topics, from mantle and lithosphere dynamics on Earth and other planets, via surface processes in the boundary layers between Earth’s surface and atmosphere, to the dynamics
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molecular and cell biology techniques, including RT-qPCR, ELISA, immunocytochemistry, and Western blotting. Experience with sample preparation, imaging, and image analysis using confocal and high-resolution
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will be further developed by the successful candidate in collaboration with the supervisory team. Main tasks The successful candidate will: develop and evaluate image-classification workflows
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of 3 years. About the project/work tasks: The project aims to develop innovative quantum sensing techniques using nitrogen-vacancy (NV) centers in diamond to study intracellular processes in stressed