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? PhD position in Algorithms and Extremal Combinatorics There is a vacancy for a PhD Research Fellow in Algorithms and Extremal Combinatorics at the Department of Informatics . The position is for a fixed
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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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participation in several European and Dutch national projects. Our key research areas include ultra-reliable low latency communications, resource allocation, digital twins, distributed massive MIMO and flexible
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to develop principled models and algorithms for distributed decision-making in complex and uncertain environments. Your research The candidate will develop a novel hierarchical control framework
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 4 hours ago
transdisciplinary research interests in: 1. Modern and future Computing systems, including photonic and quantum computing, analog neuromorphic computing, combinatorial algorithms, and continuous optimization. 2
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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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algorithms for large-scale or distributed training/Robustness, fairness, and personalization in multi-agent learning/Training efficiency and communication reduction/Distributed training of transformer models
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in the body. However, clinical PET imaging has so far been largely limited to imaging the distribution of a single radiotracer per scan. In collaboration with the Forschungszentrum Jülich, the PET
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Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD
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Job Description DTU Electro invites applications for a PhD position in digital signal processing and machine-learning-enabled continuous-variable quantum key distribution (CV-QKD). The position is