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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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. 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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of the proposed solutions. Activities will include the implementation and evaluation of algorithms for signal processing, detection, correlation, localization, and tracking of acoustic sources, as
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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
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: - Study and improvement of developed algorithms - Automation of the model
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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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validation of the solutions being created. Activities will include the selection and integration of sensors and data acquisition systems, the implementation of algorithms for data compression and analysis, and
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validate a comprehensive genetic panel for the molecular diagnosis of pituitary diseases. Using next-generation sequencing (NGS) technologies and a bioinformatics algorithm, the panel will identify genetic
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validate a comprehensive genetic panel for the molecular diagnosis of pituitary diseases. Using next-generation sequencing (NGS) technologies and a bioinformatics algorithm, the panel will identify genetic