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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 21 hours ago
). This position requires hands-on software engineering expertise and the ability to develop secure, reliable, and maintainable solutions across public-cloud and on-premises environments. The role will work
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sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI
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for the Reliability, Availability, Maintainability, and Safety (RAMS) of systems, plants, and stru Where to apply Website https://aunicalogin.polimi.it/aunicalogin/getservizio.xml?id_servizio=1079 Requirements
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 13 days ago
structures, as well as a comprehensive understanding of how financial and HR data interact to support budgeting, compensation analysis, position management, and long-range financial forecasting. The position
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compliance obligations. The role provides technical and operational support across fixed assets, leased assets, tools, inventory, capital expenditure and construction-in-progress. It works closely with
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at American University are eligible to use Federal Work Study (FWS) awards, with a few exceptions. FWS funds cannot be applied to janitorial, construction, partisan or sectarian positions. For information
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electrification with grid supplied power. This position is accountable for system design, reliability engineering, asset management, and capital project leadership and support to ensure the safe, reliable, and
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School graduates over a thousand students who are ready to take on great ambitions and challenges. For more details, please view: https://www.ntu.edu.sg/eee We are looking for a highly motivated and
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the foundation is trustworthy by establishing policies and practices that make data a reliable institutional asset. This role reports to the Chief Data and AI Officer with a direct line to executive leadership
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structures with extraordinary precision and do so quickly enough to keep up with large-scale production. This creates a fascinating computational challenge: how can we infer hidden physical properties from