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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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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 analysis of large-scale omics data (e.g., proteomics, metabolomics, transcriptomics), including basic programming skills in R and/or Python Documented experience with exercise testing (e.g., testing
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and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High-dimensional and structured biological data are increasingly common in modern
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single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) is an advantage. Knowledge of data analysis methods, including statistical software and transcriptomic analysis pipelines is an advantage. Excellent
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advantage. Knowledge of data analysis methods, including statistical software and transcriptomic analysis pipelines is an advantage. Excellent written and verbal communication and presentation skills in