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Field
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interaction Research and design multi-modal foundation models to enhance robot autonomy, social perception and collaborative decision making Design and build machine learning algorithms and frameworks
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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project integrates expertise across multiple levels—from circuits and architectures to algorithms, models, and systems—and includes opportunities for radiation testing at the NASA Space Radiation Laboratory
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algorithms (convex/nonconvex, stochastic/robust, MPC) for real-time dispatch, frequency regulation, and DER coordination. Integrate data-driven and physics-informed approaches for state estimation, forecasting
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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artifacts, and developing an independent research agenda in AI for science. Core responsibilities include: Leading research on foundation models, including problem formulation, algorithmic development, and
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between various imaging modalities and multi-omics during aging and development. • Implementing computationally intensive algorithms on high-performance computational clusters
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
retrieval algorithm development with focus on using the polarimetric signals, the new FIR or sub-mm bands, and/or the ML/AI approach; (3) ML/AI application on system/pattern tracking on satellite images
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analysis across time and conditions Algorithm design and modeling. The role offers significant intellectual freedom and opportunities to shape the direction of the research. Minimum Qualifications: • PhD in
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and resilience across heterogeneous computational resources while addressing workflow requirements for scientific applications. Validate distributed intelligence algorithms at scale on ORNL's