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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
the structural and micro-scales, capturing the complex deformation mechanisms at the microstructure and the strain-gradients observed when failure occurs. Main responsibilities: Identify the main microscopic
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 2 months ago
methodologies to complex engineering systems; Development and application of advanced Scientific AI methodologies, including surrogate models, generative models, probabilistic models, and data-driven and physics
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awarding of the fellowship is dependent on the applicants' enrolment in study cycle or non-award courses of Higher Education Institutions. Preference factors: - knowledge of complex production systems
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mechanisms.; - Develop data-driven and/or causal model-based approaches for the identification of root causes leading to atypical asset degradation, considering charging systems as complex and
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Institutions. Preference factors: - Previous experience with C/C++ programming (many of the implementations to be compared are written in these languages); - Knowledge of graph theory and/or complex network
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or entropy coding; Knowledge of code optimization, parallel programming, and performance analysis, including computational complexity, throughput, latency, and memory management; Ability to work independently
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cases of industrial symbiosis in the context of complex wastewater treatment systems; - Investigating the relationships between industrial symbiosis, circularity and carbon sinks with a view to achieving
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estimation, and acoustic source localization in complex environments. The work will also provide experience in processing data obtained from DAS systems and solving inverse problems related to estimating
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.; - Develop data-driven and/or causal model-based approaches for the identification of root causes leading to atypical asset degradation, considering charging systems as complex and multidisciplinary systems
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developing solutions for locating and manipulating semi-rigid objects or complex geometry. Investigating machine learning strategies with limited data, including the generation of synthetic data in simulation