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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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pangenome graphs, and identify trait-associated structural variants. Moreover, we have developed imputation methods that provide accurate genotypes in pedigreed populations, and haplotype-based association
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: (1) automated reconstruction of a visual and geometrical 4D Digital Twin based on visual computing; (2) usage of information from digital imaging techniques for estimation and prediction of current and
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visualization systems. Research themes: The PhD student will contribute to several of the following topics: Computer-generated holography (CGH); Optical system analysis and simulation; Developing AI/ML algorithms
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assemblies in sperm in a near-native state using cryo-ET (‘visual proteomics’), (ii) developing expansion microscopy approaches for robust labeling and higher-throughput mapping of newly identified components
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, within the project "La peinture en bois - Visualising the colourful past based on the faded present". This is a multidisciplinary project that combines heritage science (e.g., SEM-EDX, XRF, FTIR-analysis
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learning models that integrate: Air pollution, weather, noise, green space, urban form; Socioeconomic and demographic data; Hospital admissions and mortality records; Develop physics-guided and graph-based
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, purification and reconstitution Targeted mutagenesis and protein structure analysis Biochemical characterization of ATPase and phosphorylation activity Cellular imaging/staining (fluorescence and electron
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quantitative and analytical skills. Experience with programming and data analysis (preferably in R; experience with Python, Julia, Matlab, or similar is also valuable). Experience in using AI to operate said
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; Socioeconomic and demographic data; Hospital admissions and mortality records; Develop physics-guided and graph-based models for high-resolution environmental exposure estimation; Build explainable AI pipelines