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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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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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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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‑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
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focuses on leveraging zebrafish as a model organism to develop and optimize genetic tools through a directed evolution pipeline, with significant therapeutic and industrial applications. Key
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. There is growing interest in microsystems that can replicate the local physiology of tumors, providing a platform to identify and optimize therapeutic candidates. Our group specializes in developing
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to efficiently transform biochar into battery-grade hard carbons with controlled characteristics and optimized electrochemical performance. The use of microwave plasma carbonisation will allow