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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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TRAINING: Literature review on anomaly detection in network data; Using deep learning to detect anomalies in network data flows.; 4. REQUIRED PROFILE: Admission requirements: Degree in Computer Engineering
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requirements: Student currently enrolled in a Bachelor's degree program in Electrical and Computer Engineering, Computer Engineering, Artificial Intelligence, or a graduate in the same fields. The awarding
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results. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Develop machine learning-based models from data.; - Validate the developed models with real data.; - Publicize the work in international
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AND TRAINING: - survey and analyze the state of the art in emerging wireless networks, including simulation aspects using real data assimilation, Machine Learning, and digital twin approaches
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the usability and user experience of the solutions - Prepare activity reports and scientific articles 4. REQUIRED PROFILE: Admission requirements: PhD student, with a completed master's degree The awarding
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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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infrastructure. More specifically, this research grant aims to achieve the following objectives:; Expand scientific and technical knowledge in the field of real-time simulation, human–machine interfaces (HMI), and
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infrastructure. More specifically, this research grant aims to achieve the following objectives:; • Expand scientific and technical knowledge in the field of real-time simulation, SCADA systems, human–machine
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awarded to students who are enrolled in non-award courses, or up to four years, in the cases of students enrolled in a PhD. Scientific advisor: João Tiago Paulo Workplace: INESC TEC, Braga, Portugal