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that manual analysis cannot match: tomographic volumes, surface meshes, point clouds, and derived geometric measurements across hundreds or thousands of specimens. The successful candidate will build
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cancer genomics resources and databases, including The Cancer Genome Atlas, cBioPortal, Genomic Data Commons, dbGaP, GEO, and related resources. Experience with high-performance computing, cloud-based
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computing, cloud-based computing environments, workflow-management systems, containers, and/or software development practices. Experience with machine learning, predictive modeling, or artificial intelligence
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biological data. · Proficiency in R and/or Python for data analysis and visualization. · Experience working with large datasets in an HPC or cloud computing environment. · Demonstrated ability to work