240 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "ETH Zürich" positions at Oak Ridge National Laboratory in United States
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direction, mature technologies from lab to field, and integrate sensors, data pipelines, and analytics into operational environments. This position requires deep practical experience in power engineering and
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compliant, mission-focused, and employee-centered workplace. Major Duties/Responsibilities: Workforce Data & Reporting: Generate and analyze workforce data and standard HR reports to support HR decision
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objectives into robust pipelines, automate job orchestration and data movement, and optimize end-to-end workflow performance on large-scale Linux-based HPC environments and hybrid cloud/HPC platforms. Job
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records, information systems, and process improvement. This role focuses on modern records practices, including electronic records management systems, digitization initiatives, system integrations, and
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of risks or blockers and ensure they are documented in the project's risk log for Project Lead’s review and action. Information Management : Maintain organized and accurate project documentation, serving as
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and analysis techniques to support these experimental efforts. Contribute to the development of ML/AI tools for nuclear physics and nuclear data applications. Publish papers, reports, and act as
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workflows, and data infrastructure to accelerate discovery in Populus genomics and the characterization of Populus-associated microbial communities. The successful candidate will design and implement scalable
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Sensing Group (RSG). RSG is part of the Oak Ridge National Laboratory (ORNL) and seeks to hire qualified candidates to support photogrammetry and 3D computer vision research initiatives with emphasis
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and image data processing. Specific knowledge related to neural network design, training, and optimization is required. You will be joining a group with core expertise in sensor data analytics from
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. This section will advance the integration of high‑performance computing (HPC), artificial intelligence (AI), data science, and automation with experimental biosciences to enable predictive, scalable, and AI