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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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. 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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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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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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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 days ago
data sharing (preferred); good knowledge of machine learning algorithms; proficiency in high-level programming languages (e.g., C++, Python, C#, etc.) (preferred); knowledge of database formulation and
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 days ago
field. This project involves: general literature review on information retrieval strategies using emergent tools, such as machine learning, semantic embeddings, etc… for the particular case of waste-to
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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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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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:………..………………………………………………………………………………………………………………………………………… Management, with particular focus on the intersection of: Machine Learning Causal Inference Applied Field Experimentation Scientific Areas: Management, Economics, Information Systems, Computer Science, Data