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that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance and edge computing; The design of architectures
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PhD Studentship: Distributed and Lightweight Large Language Models for Aerial 6G Spectrum Management
resources. To fill this gap, this proposal aims to design novel distributed and lightweight LLMs for spectrum management in aerial 6G networks. Specifically, the project will design wireless-aware data
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experimental facilities. You will be responsible for developing energy-efficient, physics-aware algorithms designed for distributed learning across both high-performance and edge computing environments. You will
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action discretization, exploration parameters, and decision intervals significantly affects algorithmic convergence and system performance. Distributional RL techniques, such as quantile regression and
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is to implement algorithms, models, and software applications that can be translated to clinical use. Job Description Primary Duties & Responsibilities: Information on being a postdoc at WashU in St
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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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of molecular dynamics algorithms in GROMACS. The main focus will be on mixed precision techniques as part of the GANANA EU-India HPC partnership. This R&D work will involve: Design and development of mixed
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topics as well as related topics (e.g., privacy, trustworthiness, mobile/wireless security, distributed learning and cloud security, software and system security). Areas of theoretical computer science
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for full-time or part-time employment in a European research program in the field of " Photonics for Quantum Key Distribution". The successful candidate(s) will work on a €7.5 million research project
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computing. This will include, but is not limited to, the design of distributed quantum algorithms, circuits, and error correction, as well as the interplay between circuit optimization and circuit