908 distributed-algorithm-"Newcastle-University"-"Newcastle-University" Postdoctoral positions
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Field
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algorithms for critical equipment (e.g., compressors, reaction kettles). Research advanced process control strategies and deploy them on the center's distributed control systems and edge computing devices
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About the Opportunity The Postdoctoral Research Associate advances research in mathematical modeling, optimization, and stochastic analysis for large-scale and distributed systems. The role focuses
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Experience: In the development of medical devices in vision sciences. Implementation of psychophysical algorithms for vision. Design and analysis of clinical studies. Experience in functions similar to those
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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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: Professor Zhongwen Zhan) to design, build, and validate a miniaturized Distributed Acoustic Sensing (DAS) system for the lunar environment. Key responsibilities: - Architect and prototype a low-SWaP (Size
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trigger reconstruction architectures for future particle collider experiments, based on deep learning models distributed across multiple hardware processing stages. The mission of this position, based
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related definitions. Knowledge of SOTA federated learning algorithms. Knowledge of distributed optimization and consensus algorithms. Knowledge of large models and hyper-parameter optimization. Knowledge
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 18 days ago
, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language and generative models. Develop novel algorithms for generative modeling tasks and optimize
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feedback. The research connects to international initiatives and offers opportunities to collaborate with leading European groups developing open and trustworthy AI systems at scale. Out-of-Distribution
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learn a monolithic, “black-box” world model, often using a large neural network as function approximators. While these models can be highly effective for prediction within their training distribution