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Computer engineering, Data Science, Mathematics or equivalent, with focus on energy modelling and electricity markets. Proven experience (>2 years) in energy systems modelling, machine learning or artificial
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Postdoctoral researcher in marine ecosystems modelling for the Marine and Continental Waters Program
of machine learning and AI algorithms and methods. Knowledge of species distribution models. Catalan and Spanish are valued LanguagesENGLISHLevelGood Research FieldOtherYears of Research Experience1 - 4
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in advanced methods for the analysis and classification of EEG and auditory signals. The group of the project is multidisciplinary, with experts in signal processing, machine learning, acoustics and
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. We will analyse systems based on deep reinforcement learning and transfer learning, as well as distributed architectures that use federated learning. This work package outlines the tasks required
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model building, biomedical text mining, machine translation and unsupervised learning for under-resourced languages and domains. It has been entrusted by the Spanish and the Catalan governments with
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the following objective included in this R&D&i Project “Knowledge Generation” PID2022-136435NB-I00, funded by MICIN/AEI/10.13039/ 501100011033 and by “ERDF A way of making Europe”, EU: - “O8. Speeding up Machine
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of AI and machine learning techniques, including handwritten text recognition and probabilistic indexing, to improve access to historical sources. Ensuring the archive is sustainable, accessible, and
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experience on wireless communication networks demonstrators, as well as cutting-edge knowledge on machine learning/AI-related techniques for PHY layer communication systems. For more information about the WAVE
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sequences, networks, trajectories, images, etc. - Design, programming, optimization, and parallelization of machine learning algorithms. - Search in repositories and bioinformatics of DNA sequences
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the job profile (sections 2 and 3 of this annex), as well as to: Programming in Python Use of machine learning libraries: TensorFlow, PyTorch and scikit-learn Development of supervised and unsupervised