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, energy consumption, and accuracy.; ; Training deep learning models, especially in LLMs, faces critical challenges that compromise the optimal use of GPUs. These bottlenecks result in poor computational
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: - prepare the requirements specification for a software module that allows the use of pre-trained large language models (Large Language Model); - containerization and availability of trained models
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workload’s data (e.g., Deep Learning, Large Language Models) while addressing the I/O interference and fairness challenges faced by current distributed infrastructures, where storage resources are being shared
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courses of Higher Education Institutions. Preference factors: • Experience with computational simulation models / MATLAB/Simulink.; • Knowledge of industrial-grade communication protocols (Modbus TCP, IEC
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developing synthetic datasets using simulation environments. Familiarity with the theory and implementation of diffusion models and GANNs applied to the same context. Minimum requirements: • Knowledge
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generative models for the creation of human movement datasets for training AI models; - Prepare activity reports and scientific articles. 4. REQUIRED PROFILE: Admission requirements: - Master's student, with a
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learning models for generating artificial data using generative models. The result will be high-fidelity medical data. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - extend the knowledge
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protocols for data collection with motion capture systems and curation of the resulting data - Design generative models for the creation of human movement datasets for training AI models - Evaluate
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/pagamento-propinas-bolseirosEN ) The grant holder will benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: The overall vision of the ATE is to deploy and demonstrate a set of business models
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architectures for the energy and power management system on ships Develop control and dispatch strategies for hybrid microgrids, taking into account the specific power and energy requirements of ships Modeling