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Field
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role in supporting OTMC’s portfolio of translational and pre-clinical research studies, including next-generation single cell and spatial transcriptomic, epigenomic and proteomic data generation
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(and potentially proteomic) bioinformatics, small input RNA-seq, and high-level imaging, as well as research background in related studies. Junior applicants, such as recent Ph.D. graduates with 0 or 1
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for: Analysis of bulk and single cell RNA-sequencing, spatial transcriptomics, and proteomics datasets Integration of experimental model data with public and clinical datasets Statistical modeling and survival
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methods for metabolomics and spatial multi-omics data, integrating multi-modal datasets (genomic, transcriptomic, proteomic, and clinical), and identifying molecular mechanisms, biomarkers, and risk
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projects with other cohorts. The AGES-Reykjavik study is one of the world’s most deeply phenotyped aging cohorts and includes genetic data, large-scale proteomics, DNA methylation, and rich longitudinal
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(structural, functional, and/or related preprocessing pipelines). Experience with proteomic data or other high-dimensional molecular datasets. Experience harmonizing data across cohorts; familiarity with
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with cell culture skills (human or animal models) with an emphasis on organoid and stem cell biology Analytical and molecular techniques (i.e. qPCR, ELISA, multiplex genomic and proteomic assays
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data from genomics, proteomics, and epigenomics data generated across various cancer types Perform in-depth analyses across different omics platforms to identify phenotype-genotype associations Develop
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research assistants, fostering a productive and supportive lab environment. Analyze, interpret, and integrate large-scale datasets (e.g., genomics, transcriptomics, proteomics) to generate actionable
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-parameter flow cytometry, and sample preparation for downstream genomic, proteomic, and spatial analyses. Experimental Design and Data Handling Assist in planning experiments, optimizing protocols, and