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learning on large-scale HPC systems Scalable and energy-efficient AI training algorithms Image reconstruction, segmentation, and spatiotemporal modeling High-performance computing for large-scale AI and
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applications for a Postdoctoral Researcher position. The selected candidate will undertake both collaborative and self-directed research on mathematical modeling, optimization and simulation models/algorithms
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Integration: Integrate multi-modal data (e.g., single-cell RNA sequencing, spatial omics) to uncover new insights into cancer biology and therapy. Algorithm Development: Create and refine algorithms
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on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems
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, and analysis by using concepts and methods from machine learning, including pattern recognition, graphs, and complex networks. ** Specific Research Areas: * Develop concepts and algorithms to analyze
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 5 hours ago
learning algorithms Strong mathematical foundation specifically in linear algebra, differential equations, and optimization Solid communication and teamwork skills Point of Contact Mikeala Eligibility
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have: experience with spiking neural networks and neuromorphic architectures; deep knowledge about our brain and how it makes decisions; coding reinforcement learning algorithms/feedback systems and
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distributed quantum computing. The center includes other quantum faculty, and conducts a wide range of collaborative quantum research in the areas of quantum computing, quantum algorithms and complexity
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with signal processing and control algorithms. Excellent communication skills in English (written and spoken); ability to work in interdisciplinary teams and to publish scientific results. A strong
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members. Participate in traffic scenario generation project and pedestrian modeling project. Develop sophisticated AI-driven algorithms that create realistic, safety-critical test scenarios for autonomous