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for the analysis of complex experimental data; • Development and implementation of strategies for multiomics data integration and systems biology; • Use of Python and R for statistical analysis, machine learning
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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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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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. 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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the deadline for applications is required, in the contracting phase, including those resulting from academic degree recognition processes. Preferred factors: Knowledge of Machine and Deep Learning; Knowledge in
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from academic degree recognition processes. Preferred factors: Knowledge of Machine and Deep Learning; Knowledge in data exploration and processing; Knowledge of Generative AI models n mainly LLM's
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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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statistics and/or machine learning, particularly in multivariate analysis (e.g., principal component analysis, regression) and data integration methods; Experience in scientific research activities
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Operations & Technology Knowledge Center, with the following conditions: MAIN FIELD:………..…………………………………………………………………………………………… Management, with particular focus on the intersection of: Machine Learning Causal
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19 Sep 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal