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Field
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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sampling-based and reinforcement learning-based motion planning algorithms for multiple robotic arms in automotive manufacturing, including testing, performance evaluation in both simulation and actual
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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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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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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management system for the e-vessel microgrid for coordinated control of multiple energy sources considering cost, carbon, and operation constraints. Operate the simulated electrical model on a real-time system
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which