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Computing (HPC) system architecture and intelligent storage design. The candidate will contribute to research and development efforts in scalable storage and memory architectures, telemetry-driven system
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Confidential Computing and Secure Multi-tenancy. The candidate will be able to make research contributions in areas of system software architectures to support secure computing enclaves on large scale HPC and
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secure enclave architectures and federated learning approaches. Background in developing reproducible pipelines with validation, provenance tracking, and schema consistency checks. Publications in relevant
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are especially interested in candidates with strong technical expertise in AI architecture design (e.g., Vision Transformers, foundation models, and federated learning), scalable computing on leadership-class
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Integration group within NCCS. Specific areas of research interest include: Flexible, composable storage service architectures that expose useful data container abstractions and interfaces for applications
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engineering, architecture, architectural engineering, or related field completed within the last five years. Experience in building energy modeling and analysis. Deep understanding of building thermal physics
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applications, (2) design and architecture of integrated, hybrid, atomistic simulation software packages (e.g., LAMMPS) and DL models, and (3) documentation, verification and validation, and software quality
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for state-of-the-art high performance computing architectures. Study the dynamics and properties of lattice models of nonequilibrium quantum materials using innovative computational techniques. Collaborate
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
ML concepts and architectures and hands-on experience with open-source AI/ML packages (such as pytorch, scikit-learn, tensorflow, JAX etc.). Preferred Qualifications: Good grasp of concepts in solid
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the development of AI architecture for holistic genomic photosynthesis modeling. Evaluate performances of AI genomic photosynthesis models. Report advances to program management and broader scientific communities