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Field
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geologic media As for programming, we prefer familiarity with MATLAB, Python, and C++. Prior experience with high-performance computing (HPC) clusters and Unix operating systems is advantageous. We welcome
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disorders. The successful candidate will apply cutting-edge techniques in spatial and single-cell transcriptomics to study the effects of gene knockouts across multiple scales, contributing to a comprehensive
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Mendelian genomics and multiple NIH-funded Precision Medicine initiatives. This position offers an exciting opportunity to contribute to groundbreaking research in genomic medicine, working at the forefront
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the following objectives: 1. Characterize 3-D Urban Structure and Change: Utilize data from multiple remote-sensing platforms and deep learning algorithms to generate high-resolution maps of 3-D urban structure
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candidate will receive training in analyses of single-cell and bulk-tissue multi-omics data and be expected to participate in multiple projects involving design, implementation and integration of large-scale
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coding environments (e.g., R, Python). Ability to work collaboratively with a team of diverse backgrounds. Excellent written and verbal communication skills. Experience in processing large data sets
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preferred. Knowledge of epidemiological modeling, and proficiency in multiple programming/analytical computer languages (Python, R, Matlab, etc.) a consideration. Brief Description of Duties (Senior
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preferred. Knowledge of epidemiological modeling, and proficiency in multiple programming/analytical computer languages (Python, R, Matlab, etc.) a consideration. Brief Description of Duties (Senior
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University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations
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skills in Python Demonstrated data analysis and visualization expertise Experience working in high-performance computing environments Familiarity with data analysis and visualization tools Preferred