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Because of the drastically increasing demand from AI/ML applications, the computing hardware industry has gravitated towards data formats narrower than the IEEE double format that most computational
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of experimental quantum communication hardware development, optical memory qubit characterization, and fiber-based networking demonstrations using novel memory qubits. The goal is to employ the natural telecom
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, co-simulation, or hardware-relevant environments. Primary Responsibilities: Develop machine learning and AI methods for control, optimization, and cyber-resilient operation of distribution systems, DER
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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contributions to experiments at Fermilab (SeaQuest, SpinQuest) and PSI (MUSE) Detector hardware leadership, including the ALERT time-of-flight detector, the ePIC Barrel Imaging Calorimeter, and the SoLID detector
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involved in SpinQuest at Fermilab and the MUSE experiment at PSI. Our hardware program includes the ePIC Barrel Imaging Calorimeter, and instrumentation R&D such as a polarized lithium-ion source for EIC
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precision opto-mechanical hardware, and compliance with APS safety and training requirements. Position Requirements Ph.D. completed within the last 0–5 years in Materials Science & Engineering, Physics
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science for quantum information hardware with the industrially mature solid state platforms of silicon/silicon germanium and silicon carbide spin qubits. The position will focus on heterogeneous integration
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contributions to experiments at Fermilab (SeaQuest, SpinQuest) and PSI (MUSE) Detector hardware leadership, including the ALERT time-of-flight detector, the ePIC Barrel Imaging Calorimeter, and the SoLID detector