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robust procedures for sensor calibration, coordinate-frame management, timestamp synchronization, and data acquisition across heterogeneous devices. Develop and evaluate multi-sensor localization and
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optimization Integrate real-time sensor data and telemetry into closed-loop control frameworks Test and validate control algorithms through simulation, hardware-in-the-loop testing, and/or physical testbeds
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, machine learning, and statistical modeling methods for multimodal manufacturing datasets, including sensor streams, in-process signals, post-process characterization data, simulation outputs, and
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analyzing interferometric sensor data such as extrinsic Fabry-Perot interferometry, optical frequency-domain reflectometry, optical time-domain reflectometry, or distributed acoustic sensing. Proficiency
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integrating phenotypic or imaging-based datasets (e.g., RGB, chlorophyll fluorescence, root imaging) with biogeochemical, microbial, or environmental sensor data. Experience analyzing large datasets
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manufacturing, process physics, thermodynamics, and/or metallurgy to interpret sensor data in a materials science context. An excellent record of productive and creative research demonstrated by publications
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measurements at comparable sample illumination and acquisition time, progressing from reference samples to biological specimens. Collaborate with biology teams to image cells and tissues while
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opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. The
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derived land-cover and vegetation product, and experience with in-the-cloud image processing Experience with geospatial foundation models or pretrained Earth-observation models. Experience with
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, mass spectrometry, and image data are required. Understanding and hands-on experiences in vacuum instruments are expected. Skills and hands-on experience in nano- and micro-fabrication of MEMS and