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Mission: Support research to improve the management of renewable energies. Functions to be developed: Develop machine learning algorithms. Implement and validate computational models. Plan networks with
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methods for uncertain data, including variational autoencoders for the generation of coherent surrogate datasets with incomplete or imprecise information. Implementation of similarity-based learning and one
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Research Infrastructure? No Offer Description Este contrato es parte del proyecto Machine learning optimization of heat pump architectures based on wireless power transfer sensors with high gain antennas
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The Center for Machine Learning Research (CMLR) is a newly founded interdisciplinary research center at Peking University. Its goal is to advance machine learning-related research across a wide
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discovery. This position targets researchers whose primary methodological contribution lies in artificial intelligence, machine learning, probabilistic modeling, or their mathematical foundations, rather than
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-based principles. The successful candidate will train, benchmark, and develop deep learning architectures. They will work in high-performance computing environments and apply expertise in statistical
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flexible fitting of mechanistic and statistical models to the data. Our current focus is on generating datasets of sufficient size and diversity to train machine learning models to accurately predict how
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and prompt templates. Design and implement AI algorithms and systems, including but not limited to neural networks and machine learning, computer vision, intelligent decision-making, recommender systems
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: mathematics (algebra, geometry, probability theory, optimization theory), statistics, theoretical physics, theory of computation. Cutting-edge Disciplines: deep learning theory, generative models, graph neural
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School of Quantum at the University of Chinese Academy of Sciences (UCAS) | Spain | about 4 hours ago
Ph.D. or equivalent degree and have demonstrated ability to conduct outstanding research. Successful candidates are expected to carry out independent and innovative research, be able to teach both