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learning y deep learning para el análisis de datos de observación de la Tierra. Application of machine learning and deep learning algorithms for Earth Observation data analysis. 5. Manejo de plataformas
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ion transport, degradation, and safety. The doctoral candidate will use quantum-mechanical simulations, complemented by advanced machine-learned potentials, to model how charge, atoms, ions, and defects
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. The successful candidate will be involved in the gravitational wave astronomy research area as part of the GRAVITY research group, within the framework of the project "Ground-based Discovery Machines
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machine learning models (ML). Integrating and running interface usability tests. Running pilot trials and writing scientific articles. PI: Dr. Federico Javier Leguizamo Barroso. Funding body: Govern de les
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to research projects involving big data analytics, artificial intelligence, and machine learning applied to stroke care. Support the development and validation of predictive models and clinical decision-support
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results in peer-reviewed journals, present them at conferences, and contribute to project reporting. Work with the interdisciplinary team of the group (theory, computation, machine learning) and support
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, atomistic simulations, and machine-learning techniques, the postdoctoral researcher will develop predictive models of mineral carbonation processes and provide fundamental insight to guide and complement
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. -Estimation of environmental indicators related to biodiversity and ecological connectivity. -Design, development, and benchmarking of machine learning models for indicator estimation. -Design, development, and
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organization infrastructure. Application of statistical modelling, machine learning, and, when relevant, signal processing methods. Requirements for candidates: Essential: Degree or Master’s degree in Data
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Pastrana, Guillermo Suarez-Tangil. IoC Stalker: Early Detection of Indicators of Compromise. Annual Computer Security Applications Conference (ACSAC). December 9–13, 2024. (link: https://suarez