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Field
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analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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transcriptomics. Familiarity with next-generation sequencing data analysis tools. Experience working in Linux environments, including batch job management on shared computing resources. Familiarity with a variety
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). Our laboratory combines single-cell multiomics (scRNA-seq, scATAC-seq, spatial transcriptomics), cell-surface proteomics, viral vector engineering, and computational modeling to decode the development
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with computational analysis of multi-omic data (e.g., structural variants, transcriptomics) using R or Python pipelines. Mentoring and Professional Development: The postdoctoral associate will work
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transcriptomics, brain banking, and clinicopathologic correlation in disorders including Alzheimer disease, hippocampal sclerosis-associated epilepsy, autoimmune neurologic disease, and related
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biology techniques, preferably in single cell sorting (FACS), scRNA-seq, spatial transcriptomics and very strong multi-omic data analysis skills related to the microbiome/metabolome. Superb quantitative
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proteins and pathways implicated on disease pathogenesis. We are currently analyzing brain, CSF and blood, multi-omic data (transcriptomics, proteomics and metabolomics), from a large collection of
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/ phenotypic / response output assays and biomarker detection /identification in vitro and in vivo. Determining immune cell responses via FACS, high content imaging microscopy, proteomics and transcriptomics
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immunofluorescence, single cell transcriptomics, high-throughput drug screening assays, CRISPR-based approaches, in vitro tumor models, and biomedical data analysis. Mentoring: The candidate will work
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immunology, and cancer immunology using animal models and single-cell multiomics/spatial transcriptomics approaches. This postdoctoral researcher will combine wet-lab molecular biology methods, dry-lab