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: Data processing nowadays unfold along the computing continuum, that is, merge the raw compute capacity of cloud providers, while being complemented by Edge processing units, that perform computation
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scientific computing tools such as MATLAB/Simulink, PLECS, GNU Octave, Altium Designer, and KiCad; - Prior knowledge of operating systems (Linux and Windows). Minimum requirements: - Master's degree
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, communication requirements, robustness, and real-time feasibility. Algorithms will therefore be designed with deployment on embedded and edge computing platforms in mind, considering the energy and
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high-performance environments (e.g., HPC), through the development and configuration of the necessary benchmarking tools and the execution of the corresponding experiments.; 3. BRIEF PRESENTATION OF THE
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work to be developed during the fellowship is organized around two main objectives.; The first objective is to design and implement new storage features focused on performance (e.g., caching and
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optimize structural performance and predictive maintenance based on monitoring data. • Validate the proposed solutions through laboratory tests and, if possible, in real environments. • Contribute to
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availability of artificial references. The objective is to develop solutions capable of maintaining high levels of navigation accuracy and consistency over long trajectories by exploiting environmental
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, performance monitoring, and systematic evaluation of results. Objectives: Development and application of artificial intelligence algorithms for different use cases in the energy sector, e.g., forecasting
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characterization of performance in representative scenarios; - Comparison with other approaches in the literature; - Reporting of results in scientific publications 4. REQUIRED PROFILE: Admission requirements
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evaluating the predictive performance, robustness, and generalization ability of the models; - Conduct iterative cycles of testing, validation, and fine-tuning of the developed models; - Contribute to