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Field
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to 3D transistor architectures and stacked, hybrid-bonded devices, it becomes important to study the initiation and propagation of defects due to thermal and electrical phenomena (e.g., dielectric
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architecture based on coherent long range qubit shuttling Nat. Commun. 15, 4977 (2024) is currently one of our main activities. To this end, we pursue a holistic approach addressing material challenges, device
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networks, RNNs, LLMs) or in the deployment of such algorithms. Experience with specialized computational architectures such as GPUs, FPGAs, neuromorphic processors, or machine learning accelerators
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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to 3D transistor architectures and stacked, hybrid-bonded devices, non-destructive, high-resolution 3D imaging is becoming a necessity. Unfortunately, current 3D X-ray nanoimaging methods
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to 3D transistor architectures and stacked, hybrid-bonded devices, it becomes important to study the initiation and propagation of defects due to thermal and electrical phenomena (e.g., dielectric
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environment. Preferred Qualifications: Experience with AI/ML approaches for spatiotemporal or Earth system data, including transformers, multimodal learning, representation learning, or related architectures
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Correction Circuits Design of parametric processes and quantum control of driven non-linear quantum devices Quantum computer architecture, hardware- and noise-aware compilers Computation with Dynamic Circuits
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docking to investigate viral spike/capsid architecture, thermal stability, and immune epitope accessibility. AI/Deep Learning: ML/DL model development (e.g., PyTorch, Lightning) for predicting antigenicity
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to architectures, algorithms, and applications. Titled "Exploring Atomic Ensemble Qubits for Distributed Quantum Computing," this LBNL-funded initiative brings together experts from the ESnet, NERSC, and AMCR