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is necessary to: 1) explore platforms and frameworks that support the development and implementation of the models needed to detect and predict leaks; 2) process real and simulated data that will be
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, design, implement, and validate an optimization system for scheduling group classes within the Koachy platform. The specific goals include: Develop attendance prediction models for different types
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. Given their importance, continuous monitoring and fault diagnostics are crucial—especially as machine learning algorithms play an increasingly prominent role in predictive maintenance and reliability
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of establishing relationships between signal sources and predicting commands; 6. Design of machine learning and adaptive models that ensure the continuous evolution of the system, increasing the autonomy and
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Institute of Systems and Robotics-Faculty of Sciences and Technology of the University of Coimbra | Portugal | 8 days ago
-side flexibility options to maximize community-level renewable integration. These controllers will be co-designed with machine-learning forecasting models that will anticipate consumption needs and
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with simulation techniques, energy efficiency models, large-scale energy consumption data, machine learning techniques and interpretation (unsupervised); - Education, experience and research orientation
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plan: The work consists of developing models for the prediction of biological control agents (BCAs), using different approaches: Machine Learning (random forests, support vector machines, lasso), Deep
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of Machine Learning techniques. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC), based
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Institute of Systems and Robotics, Faculty of Sciences and Technology of the University of Coimbra | Portugal | about 1 month ago
Engineering Research Field Engineering » Electrical engineering Engineering » Electronic engineering Engineering » Mechanical engineering Engineering » Computer engineering Researcher Profile First Stage
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processes. Preferred factors: Knowledge of Machine and Deep Learning; Knowledge in data exploration and processing; Knowledge of Generative AI models n mainly LLM's; Knowledge of satisfaction model analysis