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Number AE2026-0029 Is the Job related to staff position within a Research Infrastructure? No Offer Description Portuguese version: https://repositorio.inesctec.pt/editais/pt/AE2026-0029.pdf CALL FOR GRANT
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for this grant: Requirement 1: - Be a student enrolled in a doctoral program in the area of Materials science, Machine Learning computational science, Coating and surface engineering a requirement to be duly
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Wrocław University of Science and Technology / Faculty of Information and Telecommunication Technology | Poland | about 2 months ago
creating a semantic repository for storing and indexing tax documents, designing machine learning algorithms to represent data in embedding spaces, and building tools for semantic search, interpretation
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | about 2 months ago
Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. The Department of AI4Quantum – Machine Learning for Quantum Simulation and Computing is looking for a PhD
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for applications for one research grant within the framework of project ISA4RL - Integrating Instance Space Analysis with Auto-Reinforcement Learning for Adaptive Algorithm Selection and Configuration
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 2 months ago
is mandatory to formalize applications with the submission of the following documents: B1 Form – Fellowship application (https://ist-id.pt/recursos-humanos/bolseiros/#documentos-relacionados
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than 14/20 (1 point); B. Knowledge of Interactive Systems Design, Cyber-Physical Systems, Predictive Maintenance Systems, Automation, Machine Learning and Artificial Intelligence, Sensor Networks
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using machine learning; Interpretation of soil profiles and moisture maps. Development and validation of digital tools: Support in building georeferenced web interfaces for data visualization
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, according as defined by FCT, I.P. (https://www.fct.pt/apoios/bolsas/valores ). Selection process Selection methods: Eligible applications will be scored on a scale from 0 to 20 points, taking into account the
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-vision algorithms with edge-computing processing for the automatic detection of non-conformities. Machine-learning techniques will be applied to optimize cutting parameters, and the module will be