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Field
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and testing software in high level languages; writing and testing logic in hardware description languages; developing and testing signal processing algorithms from concept to implementation; and
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electrochemical sensors for biomarkers monitoring”, project code PN-IV-P1-PCE-2023-0198, ref. no. 91PCE/01.07.2025, carried out within the Laboratory for Quality Control and Process Monitoring – LaborQ. Where
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engineering challenges in structural health monitoring systems. Apply industry design standards and practices across both hardware and software development. Work with a wide variety of sensors, including
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complicates both learning and inference processes. Another challenge is that dynamic structured data are generated by a variety of sensors and infrastructures that continuously produce, disseminate, and store
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, sensor failures, or the aggregation of datasets from multiple sources. There is a rich literature on how to impute missing values, for example, considering the EM algorithm [Dempster et al., 1977], low
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image analysis, fusion of multi-sensor data (e.g., resistivity + acoustic + NMR) for real-time subsurface characterization, AI-enabled physiological monitoring using multimodal wearable sensors, federated
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portfolio in AI4Science. The Computational Sciences Department at PPPL was formed to provide a focus for computational physics and engineering. We specialize in algorithms and applied mathematics, data
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research in PPPL-relevant AI4Science topics. The Computational Sciences Department at PPPL was formed to provide a focus for computational physics and engineering. We specialize in algorithms and applied
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quality, and real-time AI performance. This research hub, tackles the intricate challenges of cyber-disturbances and data quality in Edge Computing (EC) environments supporting AI algorithms. The role
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/INS systems, RGB-D cameras, LiDAR, radar, IR/hyperspectral cameras, and other sensors. Understanding and experience with development of electrical wiring diagrams, schematics and installation drawings