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Field
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, including hybrid simulations coupling machine learning with numerical methods, multiscale discretization, nonlocal closure modeling, structure preservation, multilevel and multifidelity machine learning
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at the APS, integrating x-ray optics and wave propagation models with realistic sample simulations based on dislocation dynamics and molecular dynamics of relevant materials. Significant attention needs
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design, modeling, mapping, coastal planning and management, or related activities. Demonstrated understanding of interactions between the built environment, coastal processes, and natural environments
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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-funded R01 project to develop and disseminate computational neuron models and their integration with TES and TMS electric field simulations in the popular SimNIBS software package. This project is a
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surgical procedures, mouse behavior, extensive knowledge of the basal ganglia and striatum and amygdala. Experience with anterograde and retrograde circuit tracing. Experience with mouse models of compulsive
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, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing ● Emerging AI/ML models and hardware
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increased risk of cardiovascular complications. Using molecular and genetic techniques in transgenic murine models, MPN patient-derived induced pluripotent stem cell lines, and primary patient samples, we
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the dissolution capability of microneedle patches and transdermal drug release by plasma sample analysis focused on animal models. Finally, the research plan will include a component of simulation to describe
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and optimization of measurement-based quantum computing protocols for quantum simulation of quantum many-body models. Preference will be given to candidates familiar with the stabilizer formalism and