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. Hands-on experience integrating sensors, actuators, computing hardware, or communications interfaces on a mobile robotic platform. Demonstrated research or development experience in one or more of the
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backgrounds such as materials science, chemistry, data science, and robotics along with close interactions with industry partners. These engagements will play a vital role in ensuring success of programs and
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advanced control strategies (e.g., model predictive control, adaptive control, feedback/feedforward control, ML-based controls) for thermal system optimization Integrate real-time sensor data and telemetry
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, root imaging) with biogeochemical, microbial, or environmental sensor data. Experience analyzing large datasets in reproducible formats (R, etc.) Demonstrated interdisciplinary and systems-level approach
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manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and digital twin data. Develop, integrate, and evaluate AI/ML models for anomaly
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characterization, probing, thermal testing, or dynamic compliance testing. Experience integrating sensors, data acquisition systems, and instrumentation into machining or manufacturing systems. Experience with data
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changing needs For additional information, contact Dr Adam Guss (https://www.ornl.gov/staff-profile/adam-m-guss ; email address [email protected] ) or Dr Carrie Eckert (https://www.ornl.gov/staff-profile
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to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Benefits at ORNL: UT Battelle
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to ORNL's Research Code of Conduct. Our full code of conduct, and a statement by the Lab Director's office can be found here: https://www.ornl.gov/content/research-integrity Benefits at ORNL: UT Battelle