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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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platform. The successful candidate will (1) integrate heterogeneous sensors and onboard computing hardware; (2) develop methods for LiDAR-based simultaneous localization and mapping (SLAM), autonomous
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sensor data in a materials science context. An excellent record of productive and creative research demonstrated by publications in peer-reviewed journal papers. Excellent written and oral
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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