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of foundation codes. Handling spatio-temporal statistics of forecast uncertainty will be a key consideration. This kind of downscaling with machine-learning methods is a rapidly advancing field and it is an
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large AI models or their applications. Current PLI projects involve work with language, images, multimodal data, science/math, code, and scientific data. PLI is also focused on AI safety, fairness, and
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development and regeneration. Our research combines in vivo genetic models, in vitro organoid systems, advanced imaging, and high-dimensional sequencing approaches to uncover fundamental principles by which
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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strategies, SiP and chiplet architecture Fault tolerance, robustness and reliability Functional / Cryptographic agility in hardware Prototype and design new architectural innovations, including chiplet
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chiplet architecture Fault tolerance, robustness and reliability Functional / Cryptographic agility in hardware Prototype and design new architectural innovations, including chiplet oriented architectures
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cortical neurons, functional loss-of-function approaches, single-nucleus RNA sequencing (snRNA-seq), xenotransplantation, and advanced in vivo imaging (e.g., 2-photon calcium imaging), the aim is to uncover