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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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new data becomes available, while preserving previously acquired knowledge. A key objective is to design signal representations and learning mechanisms that enable stable adaptation without forgetting
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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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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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methods to increase knowledge about why metallic materials are worn? We are looking for a PhD student to join our team working on developing a measurement method to be able to quantify what happens inside a
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than 1,800 employees and nearly 21,600 students. Do you want to contribute to the development of sustainable materials and work on developing advanced measurement methods to increase knowledge about why
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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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that fundamentally transforms our knowledge about how cells function by peering into their molecular components in time and space, from single molecules to native tissue environments. About the Project – DDLS Data