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, and conduct numerical structural geology and tectonics experiments at University of Arizona. The experiments will investigate stress-strain evolution during strike-slip faulting. Experimental results
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, Lattice QCD , Low-energy nuclear theory , Nuclear and Particle Theory, Dark Matter , Nuclear astrophysics , nuclear instrumentation or radiation detection, nuclear engineering, experimental nuclear
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opportunity to choose between two missions: • Mission 1: Improve new automated algorithmic schemes to quickly, efficiently and robustly detect and extract recorded geophysical signals related to earthquakes
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. The candidate will collaborate closely with team members to develop novel quantum computing algorithms that leverage hybrid detection methods and explore the potential of 3D cluster states for enabling fault
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machine learning tools for detection, diagnosis, and correction of sensor faults Report results in peer-reviewed publications Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with
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fundamental research and applied validation underlying a multi-chamber cancer-detection device from concept through integrated laboratory demonstration. You will develop user-oriented sample-handling and assay
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damage evolution from pristine to end-of-life and establishing ground truth via inspections. Develop deep learning pipelines for fault detection, damage-type classification, health indicator extraction
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for detecting seafloor crustal deformation using fiber-optic sensors. Similar efforts are being carried out in other regions, with the aim of achieving a broader understanding of crustal activity. The successful
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a unique opportunity to contribute to cutting-edge research on remote robotic control under network uncertainties, focusing on the development of fault-tolerant and adaptive control architectures
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Trainees, National Trainees, Interns and Visiting Researchers, as applicable; conduct fundamental research on (hybrid) quantum computing algorithms (e.g., error detection/mitigation techniques, quantum