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.; - Develop skills in artificial intelligence and machine learning techniques for analyzing operational data and detecting anomalies, using foundational model approaches (e.g., GridFM project, LF Energy
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, algorithms with a focus on traditional machine learning (shallow learning) and deep learning methodologies. Knowledge of Data Science, including the development of data analysis and visualisation pipelines. 5
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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
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devices. Minimum requirements: Advanced knowledge of machine learning models and Python tools for signal processing and machine learning. General knowledge of system architecture and APIs. Previous
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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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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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TEC. 2. OBJECTIVES: Collaborate with clinical partners in data collection and annotation Design and implement new deep learning solutions for the analysis of heart sound auscultation, electrocardiogram
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of a multi-modal dataset.; - Implementation of a software module for storing datasets according to a pre-defined standard.; - Development of routines for testing existing ML algorithms on a multimodal
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microgravity environments. The fellow will have the opportunity to acquire skills in research applied to space technology, combining theoretical analysis, simulation, and experimental validation. 4. REQUIRED
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engagement with suppliers for comparative analysis of commercial solutions.; • Participation in the installation and integration of the characterization system within the laboratory environment.; • Execution