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Field
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lengthy processing times associated with sequencing. This PhD project aims to develop innovative artificial intelligence (AI) methodologies by integrating histopathology images and RNA sequencing data
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imaging technologies, such as cryo-electron tomography (cryo-ET) and expansion microscopy, have been pivotal in advancing our understanding of the structure and function of motile cilia. In contrast
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experiments, as well as for its applications in medical imaging. For that, it collaborates with MedAustron, an ion cancer therapy and research center. Within this collaboration, we design and develop ultra-fast
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scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available to UK (Home) candidates only. Fully-supervised AI techniques have shown remarkable success in
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and image analysis (MATLAB or Python), machine learning techniques, and basic programming/coding will be a plus. Fluency in English is mandatory. Willingness to work in an inter-cultural and
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Funding for: UK/Home Students We invite applications for a fully funded PhD research scholarship in “Unsupervised Machine Learning for Cardiovascular Image Analysis”. This opportunity is available
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) Project description: The project aims to select and create plants with increased albedo, which is the ability to reflect sunlight. Hyperspectral imaging methods and plant genetic modification will be used
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. Specifically, NIR sensors, hyperspectral imaging coupled with standard or macro lens, spatially resolved spectrometry, evolving plots, and FTIR will be used for the non-invasive characterisation of raw material
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the genetic and clinical variability of PRPH2-IRD by studying a large patient cohort with detailed genetic analysis and advanced imaging techniques. The project will focus on identifying genotype–phenotype
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amphibians and other aquatic vertebrates. Using advanced imaging, neurophysiological, and molecular biological methods, we investigate the olfactory system. Furthermore, we use the olfactory network as a model