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, and systematic evaluation of results. Objectives: Development and application of artificial intelligence algorithms for different use cases in the energy sector, e.g., forecasting of renewable energy
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, memory usage, and scalability (as a function of network size and the size k of the subgraphs); - Research the most recent developments (post-2019/2021) in the field to identify any new algorithms
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. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: • Develop real-time optimization algorithms • Model multi-vector energy-water-hydrogen systems • Support the development of digital twins • Test the
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. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: • Develop optimization algorithms for predictive energy management • Model consumption, renewable generation, storage and CO₂ emissions • Test the
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underwater environments and their impact on detection and localization; Development and implementation of algorithms for filtering, detection, and temporal segmentation of acoustic signals; Evaluation
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of developed algorithms and their deployment. Support in data collection and documentation of the work performed. 4. REQUIRED PROFILE: Admission requirements: Master's degree in Biomedical Engineering
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analysis of data from wearable and clinical monitoring devices and clinical databases. Experimental evaluation of algorithms, development, and deployment. Support in data collection and documentation
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wearable and clinical monitoring devices and clinical databases. Experimental evaluation of algorithms, development, and deployment. Support in data collection and documentation of the work performed. 4
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the resilience, safety, and security of critical infrastructures as core requirements. Moreover, it will boost the development and validation of novel AI algorithms, by the European AI community, through open
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: • Develop real-time optimization algorithms; • Model multi-vector energy-water-hydrogen systems; • Support the development of digital twins; • Test the algorithms using operational data; • Prepare a technical