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Student or Scientific Assistant for Remote Sensing Data Processing and Cloud-based Workflows (f/m/d)
university Basic understanding of remote sensing and Earth observation data Familiarity with Python or R and interest in working with cloud-computing environments Ability to work independently, with attention
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. The SA-I will be responsible for maintaining and documenting computing and data systems using commercial cloud computing technologies including Docker images and Jupyter hubs and evolving these systems in
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workflows using SQL, Python, and related tools. Use cloud-based data tools (e.g., OCI, Azure) as needed for data processing and analysis. Ensure compliance with data governance standards and maintain metadata
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program offerings, invested in student engagement, and embracing current trends. The department offers degree programs in English Language and Literature; Creative & Professional Writing; Pre-Law; and
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10 Nov 2025 Job Information Organisation/Company Foundation for Research and Technology Department Institute of Electronic Structure and Laser (IESL) Research Field Physics » Computational physics
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-year contract starting September/October 2026 https://www.eu4greenfielddata.eu/ APPLICATIONS OPEN ON Juanary 5th 2026 Are you an aspiring researcher ready to drive the digital and green transition in
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Sign In Create Profile Cloud Systems Administrator United States | req24576 Apply Now Share Save Job Posted on: 11/25/2025 Back to Search Cloud Systems Administrator Posting Number req24576
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 14-Jan-26 Location: Boston, Massachusetts Type: Full-time Categories: Academic/Faculty Computer/Information Sciences
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/Experience: · Marketing Automation · Salesforce Certifications · Education Cloud or Non-Profit CRM Experience · Agile ways of working · Working in an ITIL environment What can you expect in return Our diverse
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National Aeronautics and Space Administration (NASA) | New York City, New York | United States | about 5 hours ago
representation and speed of computation for GISS climate model, particularly the treatment of cloud inhomogeneity in GCM grid scale (~200 Km) and the improvement of K-distribution parameterization for gaseous