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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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fellow will conduct research on Algorithmic Verification of Concurrent Systems within the Programming Languages, Logic, and Software Security Research Group at Aarhus University. The focus of the position
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contact, as identified by AFRL through recent past efforts. This includes the implementation of relevant algorithms and solvers for distributed GPU computing within the JAX Python library. Qualifications
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RESPONSIBILITIES Develop industrial process digital twin models based on the fusion of mechanistic and data-driven approaches. Develop predictive maintenance and fault diagnosis algorithms for critical equipment
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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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, distributed from Monday to Friday with flexible hours according to project requirements from 08:00 to 15:30 (flexible according to project requirements). Offer Starting Date 7 Nov 2025 Is the job funded through
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classical computing algorithms are NP-hard or, in general, difficult to implement. Within your application, please provide a research proposal (no more than five pages) answering the following questions: What
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 25 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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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