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the lifecycle of AI systems, from data preparation to model use, monitoring, and updating, should be aligned with human activities, organisational processes, and decision-making in industrial contexts
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methodology to distribution networks. Select benchmark grids, prepare operating scenarios and AC power-flow reference results, and define model inputs, outputs and evaluation metrics.; 2) Extend the physics
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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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infrastructures for controlled, secure, and private data sharing ; - Analysis of technologies for implementing a data space in an industrial context within ocean technologies ; - Design, development, and
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model. Solid knowledge of operating systems. Solid knowledge of distributed systems. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase
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applications.; - Advanced knowledge of 3D modeling (3D modeling, UV mapping, texturing, polygon optimization). Minimum requirements: - Experience with the Unity game engine; - Experience in creating 3D models
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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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. 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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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