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Field
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space-based astronomical image slicing spectrograph. The goal is to create and apply a 'digital twin' of the as-built system to simulate and optimize its performance. Key responsibilities include
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. Implement and optimize data representations and pipelines suitable for machine learning and uncertainty quantification. Collaborate with AI/ML experts to design and test inference methods that map
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the Italian time allocation process as PI as well. You will be expected to lead high-profile, independent research that optimally uses the capabilities of the LBTI, SHARK-NIR, and SHARK-VIS, such as (but not
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supercomputer, the world's first exascale computing system. This is a unique opportunity to engage in transformational research that advances the development of AI-ready scientific data, optimized workflows, and
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patient-specific, predictive models using real-world clinical data. The project aims to enhance understanding of disease trajectories, optimize treatment strategies, and support real-time clinical decision
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 16 hours ago
of materials Proficiency in multi-physics FE simulations, topology optimization, and working knowledge of machine learning (ML) algorithms Demonstrated exceptional problem-solving abilities, a passion for
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(toliaslab.org (link is external) ), Tirin Moore (moorelabstanford.com (link is external) ), and other collaborators at Stanford. Role & Responsibilities: Design and optimize large-scale electrophysiological and
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ecosystem recovery rates. Initialize and optimize FATES using remote-sensing constraints, run model ensembles, and evaluate vulnerability and resilience across burned landscapes. Collaborate across satellite
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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on the ONR Project “Optimal Transport-based Strategies in Waves and Dynamics” led by Prof. Christina Frederick at NJIT. Essential functions will include research into optimal transport theory to capture