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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
. Description: New techniques and instrumentation are developed and applied for remote atmospheric measurements. Current research includes cloud and aerosol studies using space-based lidars such as the Cloud
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
. Description: This opportunity is closed to applicants who are Senior Fellows (5-years or more past PhD). Clouds, in particular the frozen phase, bring about the largest challenge and uncertainties
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) Experience working across the XR system stack, including lower-level components such as sensing, networking, systems optimization, or edge/cloud integration Experience incorporating machine learning
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evaluation Knowledge in biology and genomics Experience working with large datasets and numerical computing libraries (NumPy, Pandas, SciPy) Familiarity with Linux environments, version control (Git), cloud
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development and web-based applications, back-end services and API design (e.g., FastAPI, Flask), and deploying applications in local or cloud environments. Experience working with large-scale datasets
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with PyTorch, required to have experience developing code with a team through collaborative version control Experience working with large datasets and cloud computing environments. Solid background in
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application teams and contribute to broader initiatives, including the American Science Cloud (AmSC) AI Services efforts, and to help us with design of future systems and data-management solutions to meet needs
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with high-performance computing (HPC) environment, cloud-based computation, software tools for genetic epidemiologic analyses (e.g. GWAS, whole-genome sequencing, polygenic score, Mendelian Randomization
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sequencing data, whole-genome alignments, and assemblies is preferred. Candidates should be familiar with computational biology techniques and scientific programming. Prior experience with cloud and/or high
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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