239 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "University of Kent" positions at Oak Ridge National Laboratory in United States
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challenges facing the nation. We invite applications for a Principal Engineer for Geospatial Computing Infrastructure. This dynamic and visionary leader will launch and build a next-generation Geospatial Data
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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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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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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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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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development teams Basic Qualifications: A Bachelor's degree in Computer Science, Electrical or Computer Engineering, or other field relevant to the job duties. Competency in Python and C++ programming Ability
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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
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data connectivity. This role ensures that all changes to control configurations, communications pathways, or system integrations are made in accordance with Industrial Control System / Operational
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design and implement next-generation experimental workflows that integrate instrument control, automation, data management, and advanced analytics, supporting the transition toward highly automated, AI
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Laboratory. Major Duties/Responsibilities: Develop and implement advanced time-frequency and spectral analysis techniques for high-frequency voltage/current waveform data Apply wireless PHY-layer concepts