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prediction to process optimization. The focus of this PhD project is to develop and apply machine learning methods across three interconnected tasks: 3D microstructure characterisation. The student will
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expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems, neuroscience, and safety and security
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, such as heterogeneity of data sources and communication constraints. By leveraging tools from statistical signal processing, machine learning, optimization, and mathematical modeling, the project aims
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algorithms. Our research integrates expertise from control theory, machine learning, optimization, and network science, spanning diverse application domains such as energy systems, biomedical systems
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dynamics. Particular emphasis is placed on opinion dynamics as well as distributed problems in coordination, optimization, and learning. The research encompasses both theoretical and computational aspects
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identification, optimization, or numerical methods is valuable, as is knowledge of data analysis and machine learning for complex, high-dimensional systems. Programming experience in MATLAB or Python, and an
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involve the conceptual and practical development of the methodology for electrochemically initiated time-resolved soft X-ray spectroscopy, including construction and optimization of measurement setups and
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description Machine elements include the analysis and optimization of machine components and component systems based on performance, service life, energy efficiency, reliability, and environmental impact
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the analysis and optimization of machine components and component systems based on performance, service life, energy efficiency, reliability, and environmental impact. Particular emphasis is placed on issues in
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‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future