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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 19 hours ago
distribution on a global scale. This project will focus on developing a strategy to best utilize this data in a global atmospheric data assimilation framework. Activities that would be involved in this project
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(CAITE) whose aim is twofold. First, it aims to bring AI at the edge, by providing scalability and resource efficiency through the development of cooperative, distributed AI algorithms, optimising data
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will focus on models and algorithms for the intelligent control of bidirectional energy flows, the integration of vehicles into microgrids and smart grids, and the provision of ancillary services such as
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Tenure-track Assistant and Associate Professorship positions in Data Science and Machine Learning...
Statistics section, include: Algorithms , focusing on online and approximation algorithms, graph and parameterized algorithms, string algorithms, data structures, combinatorial optimization, algorithmic
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edge AI hardware/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a
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1st February 2026 Languages English English Norsk Nynorsk English PhD Research Fellow in Algorithms and Extremal Combinatorics Apply for this job See advertisement UiB - Knowledge that shapes
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/software. Contribute to designing and evaluating scheduling algorithms for virtualized or distributed AI resources under varying load, latency, and failure conditions. Build and test a scenario generator for
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using modern architectures such as convolutional neural networks, transformers, and diffusion models. Proven experience building AI solutions using classical ML algorithms such as decision trees, gradient
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for large-scale and distributed systems. The role focuses on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position
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algorithms * Parallel algorithms and distributed computing * Parameterized complexity and structural graph theory * Random structures and randomized algorithms * Sublinear and streaming algorithms