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research spanning detector simulation, Spiking Neural Network (SNN) design, neuromorphic hardware, and data-rich experimental systems such as CMS pixel detectors, Timepix4, and novel photodetector
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problems and develop formal work package instructions for performance and documentation of repairs. Experience in the operation, calibration, and troubleshooting of radiation monitors and sensors, experience
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controls, sensors, valves and electrical motor drive controls as well as PLC installation. Test electrical systems or continuity of circuits in electrical wiring, equipment or fixtures using testing devices
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to assess technology incorporation from vendor offerings, institutional resources, and evaluate opportunities in quantum networking to enhance QHPC capabilities. Demonstrate QHPC community leadership by
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relevance to clean energy, climate resilience, and infrastructure planning. Postdocs benefit from access to world-leading high-performance computing facilities and a deeply interdisciplinary research
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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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Requisition Id 15813 Overview: We are seeking a highly motivated postdoctoral researcher with a strong background in sensor integration, data acquisition, and in situ process monitoring
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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-sensor fusion, and propagation modeling — to develop AI-enabled detection, classification, and triangulation algorithms for critical energy infrastructure applications. This position resides within
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of