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assessing risks, developing and integrating embedded systems, collaborating with government and industry partners, and delivering technical expertise and training primarily sponsored by the National Nuclear
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(e.g., supervised/unsupervised learning, embeddings, model evaluation). Familiarity with structured data, metadata, or information retrieval concepts. Interest in applying AI/ML techniques to real-world
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, circuit boards, embedded control hardware, and industrial sensors/transducers. Develop and maintain technical documentation, including electrical drawings, schematics, bills of materials, test plans, and
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system integration, as well as embedded controller development. Experience in modeling converter topologies and distribution grids, developing control algorithms, and implementing controller code is
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engineers, scientists, and technical staff. Preferred Qualifications: Experience with at least one real-time operating system (e.g., Linux RT, VxWorks, or RTEMS). Experience with embedded Linux systems
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systems (Mojo, Julia, Rust, Python), and HPC system co‑design. This position is embedded within the larger DOE ASCR ecosystem, with direct relevance to ongoing efforts, and related AI‑for‑HPC thrusts
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evaluate equipment specifications using sound technical judgment. Experience with PLC/PAC‑based control systems, industrial instrumentation, embedded control hardware, and industrial networking equipment
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embedded in applied polymer and process team. Work with industry partners to translate fundamental materials and manufacturing science into applied and scalable solutions. Lead and contribute to proposal
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code and disassembly a plus. Familiarity with embedded systems preferred. Prior use and familiarity with web or network-related tools, e.g., burpsuite and wireshark a plus. Demonstrated desire and
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). This position resides within ORNL’s Digital Communications Group in the Communications and Community Engagement Directorate and collaborates closely with communicators and leadership embedded in CCSD