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. Analyzes large data sets. Analyzes next generation sequencing data (e.g., RNS-seq, whole genome/exome sequencing). Uses state-of-the-art bioinformatical and statistical tools. Develops new statistical
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statistical analyses on large datasets to provide biological insights. Integrates mass spectrometry imaging data with other multimodal imaging techniques (e.g., spatial transcriptomics) for comprehensive
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imaging data. Conducts git versioning control of R-based lab-wide scripts for basic statistical analysis and preprocessing. Performs statistical analyses on large datasets to provide biological insights
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a related field. No experience required. Preferred Qualifications Prior experience with brain development, and/or cancer cell biology would be an asset. Experience with large-scale genomic data would
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associate to support an ethical, legal, and social implications (ELSI) analysis of the NSF funded study “Integrating Human-Derived Neural Networks and AI for Information Processing in Brain Organoids” under
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genomic analyses on large case-control cohorts to identify risk and response associations for diseases of interest. Performs integrative analyses of other molecular and omics data, such as gene expression
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, GEANT4) will be an advantage. A background in radiation oncology or medical physics will be an advantage. Excellent written and verbal communication skills. Experience with cloud computing and big data
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world leader in genomics. The primary mission of the HGSC is actionable translational research across the full spectrum of human health and disease using large-scale genomic data to drive collaborative
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dynamic and collaborative research environment. Basic data science skills including the statistical analysis of large data sets. Prior experience in one or more of these techniques: design and generation
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skills including the statistical analysis of large data sets. Prior experience in one or more of these techniques: mouse survival surgery, two-photon microscopy and analysis, patch-clamp recording, single