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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | 8 days ago
scientific data, including large-scale omics datasets, within high-performance computing (HPC) environments. Learning Objectives: Under the guidance of a mentor, you will gain skills in and learn to: Develop
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for reproducible HPC environments. Experience with CUDA-level optimization or debugging hardware-specific performance differences. Basic knowledge of protein structure, folding, or biophysics. Physical Demands
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sequence data analysis Comfort with BASH, R, and command-line usage on an HPC environments Comfort with Python3, Git, and workflow management Experience in entomological systems Point of Contact Janeen
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experience in computational biology or cancer genomics Experience with high-performance or cloud computing (e.g., HPC, AWS, GCP) At least one first-author peer-reviewed publication Strong communication and
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and MySQL databases in HPC environments for large-scale data analysis. Collaborate with interdisciplinary teams to support data-driven biological discovery. Publish scientific papers, release datasets
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of large-scale genomic and transcriptomic datasets ('big data'), with hands-on experience in high-performance computing (HPC) environments (e.g., command-line interface, scripting in R/Python, use of common
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experience in working with Linux HPCs · Experience in applying machine learning methods to genomics data analysis · Experience in navigating public databases and genomics data repositories
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cloud platforms for compute and storage. Version Control & CI/CD: Git, automated testing, deployment workflows. Experience with Linux systems, HPC, and distributed computing environments. Knowledge
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with Linux/Unix and HPC systems (SLURM) Experience with version control (Git/GitHub) Understanding of statistics for genomic analysis Preferred: Long-read sequencing analysis experience Proficiency in a
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scientific software development. Proficiency in C/C++ and Python, with experience in HPC environments (e.g., MPI/OpenMP; GPU experience a plus). Record of peer-reviewed publications appropriate to career stage