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related to magnetic materials, experience with first-principles electronic structure methods and proven expertise in developing and/or applying advanced AI/ML methods for accelerated materials discovery
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and lignocellulosic biomass into domestic fuels and chemicals. Research activities may include creating transformation methods, constructing gene‑expression “genetic parts,” and developing CRISPR‑Cas
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the necessary chemistry and processing modifications to meet target alloy properties. Apply advanced characterization and modeling techniques and make fundamental contributions to the field. Interact with other
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modeling techniques and make fundamental contributions to the field. Interact with other researchers, technicians, and students to shape and drive the research agenda. Present and report research results and
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, including automated QC and uncertainty-aware learning from sparse/noisy measurements Build hybrid mechanistic–AI models linking traits to photosynthesis, stomata, hydraulics, and respiration across
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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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Application-driven Composable Distributed Storage. The candidate will be able to make research contributions in understanding and efficient use of distributed data storage and I/O subsystems for High
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simulations. Design, develop, and validate physics-informed AI/ML models with features from electronic structure, spectroscopy to control materials growth and emerging functionalities. Develop and train agentic
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
another, work together, and measure success. Basic Qualifications: A PhD in Physics, Materials Science, Chemistry, or closely related field completed within the last 5 years. Sound understanding of advanced