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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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, specifically large language models (LLMs), and general-purpose AI become more ubiquitous, they introduce novel and complex attack surfaces. Your mission will be to proactively investigate these vulnerabilities
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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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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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, advanced studies, specialized training). Preferential factors: Previous experience in Building Information Modelling; Previous experience in civil engineering or architectural industry; Previous experience
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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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the area or area related to that requested in the tender (e.g.: postgraduate studies, advanced studies, specialized training). Preferential factors: Previous experience in Building Information Modelling
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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