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coupling systems.; • Develop technical skills in electromagnetic modeling and simulation tools, as well as in energy efficiency optimization algorithms.; • Consolidate the ability to write scientifically and
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insurance, supported by INESC TEC. 2. OBJECTIVES: • Research novel deep learning models for anatomically structured EGGIM estimation.; • Develop methods for image-level and examination-level reliability
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of generating, from SQL queries, eBPF code executable in the kernel.; 2. Query planner optimization: definition of a cost model that estimates the execution cost of each operation in kernel space
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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
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of unmet user and market needs, and evaluation of problem–solution fit to reduce innovation and commercialisation risks.; ; Contribute to strengthening innovation ecosystem capacity by fostering
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exploration of attention patterns in Transformer models. The causal interpretation of attention is contested in the literature, and the scientific contribution of this research is to provide empirical evidence
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) Development of workflows and methods enabling AI-powered decision assistants to support full human operators control under risk and model uncertainty, and considering human-AI co-learning.; 2) Develop
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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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of physiological signals (pre-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models integration and
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-processing, filtering, feature extraction in the time, frequency, and time-frequency domains). Development and validation of machine learning and deep learning models integration and analysis of data from