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algorithms; - Automation of the model customization process by conducting laboratory tests.; - Improvement of the data workflow for real-time processing and sharing.; - Data collection in experimental and real
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are:; - Developing energy consumption forecasting tools based on real data.; - Applying these tools to a use case.; - Writing reports and articles for international conferences and journals using the new models and
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, models, or protocols developed; - collaborate in the development of new communications solutions for extreme environments; - contribute to co-authored scientific publications within the scope of the work
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underwater positioning systems; b. Acoustic propagation models (propagation delays, acoustic range, "multipath", etc.); c. Error sources and correction; 2. Synchronization of remote monitoring systems; 3
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the generated data can be used in practice. A new metric to help this comparison is expected to be created. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: Test GAN models – Compare leading GANs
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: • modelling and optimisation of PCM thermal storage for buildings and industry use
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electrical engineering projects.; - Knowledge of libraries for developing and training ML models; Minimum requirements: - Knowledge of computer programming. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS
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microgrids. • Develop advanced computational simulation models, ranging from the simulation of complex and representative use cases to the detailed modeling of components such as power cables, power converters
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OF THE WORK PROGRAMME AND TRAINING: - Study current models of Human-Centered AI (HCAI); - Identify architectural patterns for integrating HCAI strategies; - Analyze and study implementations of the identified
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) videos in the context of diagnosing sleep disorders, particularly REM sleep behavior disorder (RBD). The activities to be performed will include:; 1) Training and validation of machine learning models