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access to a fully equipped state-of-the-art research facility including access to all relevant tools and HPC-enabled technologies. You will benefit from mentoring through our internal experts, have the
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, and image analysis tools and working on a computing cluster (HPC). Vivid Interest in interdisciplinary research, leading and working on projects with pathologists, medical experts, computer scientists
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, Fortran, or other relevant languages. Knowledge of statistical methods for climate data analysis. Experience with high-performance computing (HPC) environments. A strong publication record relative
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Lab researches on a variety of computer systems topics including HPC resilience, data center power management, large-scale job scheduling and performance tuning, parallel storage systems and scientific
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
data using Python, R, etc. Preferred Qualifications, Competencies, and Experience Desired qualifications are a) atmospheric and aerosol chemistry, b) proficiency in HPC, and working in a cloud computing
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developing and implementing very large deep learning models. Familiarity with high performance computing environments (e.g., HPC clusters, GPUs, Cloud resources) and managing Linux based hardware systems
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, and maintenance of bioinformatics pipelines for high-throughput genomic data analysis in high-performance computing (HPC) and cloud environments Harmonize and maintain diverse datasets and their
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verbal communication skills, with a strong record of publications in relevant fields. The ideal candidate should have at least 1-2 peer-reviewed research papers published in prominent HPC venues (e.g., SC
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-performance computing (HPC) systems. Proven track record of interdisciplinary collaboration on research projects. Strong publication record in relevant fields such as environmental science, data science, or
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(e.g. Python, R, …). Familiarity to work on a Linux computing cluster (HPC). Preferably experience in working with large medical image data. Vivid interest in the analysis of microscopy images or similar