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Employer
- Autonomous University of Madrid (Universidad Autónoma de Madrid)
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- Center for Machine Learning Research (CMLR), Peking University
- Centre for Genomic Regulation
- China National Center for Bioinformation
- Huazhong Agricultural University (HZAU)
- School of Quantum at the University of Chinese Academy of Sciences (UCAS)
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Field
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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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of medical databases. Design, implementation, and testing of deep learning and AI algorithms for processing tabular, genomic and signals (including speech and audio). Where to apply Website https://www.uam.es
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, implementation, and testing of deep learning algorithms for speech and audio processing. Implementation of systems for participation in competitive technology evaluations. Where to apply Website https://www.uam.es
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for Emerging PhDs by the Universidad Autónoma de Madrid, and funded by the Community of Madrid. Where to apply Website https://www.uam.es/uam/investigacion/ofertas-empleo/contrato-conjuntajunio2026r
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are: - Management and preprocessing of databases containing data and signals, i.e., audio, images, biomedical and genetic. - Design, implementation, and testing of deep learning and AI algorithms for processing
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of databases containing data and signals, i.e., audio, images, and biological. Design, implementation, and testing of deep learning and AI algorithms for processing audio, image and biological signals and data
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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