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combine comprehensive studies of human biopsies with targeted gene disruption in advanced preclinical models, single cell transcriptomics, and bioimaging. Expertise in wet-lab and computational biology
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transcriptomics, and bioimaging. Expertise in wet-lab and computational biology, close ties to the clinic, and a wonderful team of early career scientists give us the agility to swiftly move from
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Experience with programming in Python, R, or similar languages Background in machine learning or deep learning methods Knowledge of genomics, transcriptomics, evolutionary biology, and plant biology is an
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collaborative research into the molecular and genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design
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antitumor immunity. Methods used in the lab include molecular and cell biology, transcriptomics, epigenomics and metabolomics, bioinformatics, immune fluorescence imaging, electrophysiology, and animal
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metabolism, hormonal and stress response signaling. Preparing samples for downstream sequencing and imaging. Performing MALDI mass spectrometry and spatial transcriptomics to characterize biological
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experiments will be integrated with single-cell RNA sequencing, chromatin-accessibility analysis, spatial transcriptomics and spatial epigenomics. CRISPR-engineered patient-derived organoids, including
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emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods for analyses of omics-based data obtained in the project. Collaborate
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analysis, spatial transcriptomics and spatial epigenomics. CRISPR-engineered patient-derived organoids, including lineage reporters and targeted genetic perturbations, will be used to trace epithelial cell