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tensor network algorithm development and application. Competency with common scientific programming languages such as C++, Python, and/or Julia and version control systems. Experience in parallel
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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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, algorithm design, probability theory, privacy definitions, and apply it to develop efficient privacy preserved federated learning model. Communicate and coordinate experimental results with other
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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