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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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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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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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‑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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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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on developing biochar as a sustainable feedstock for hard carbon anodes in sodium-ion batteries. In collaboration with Besca AB (https://www.bescacarbon.com/ ), this project explores microwave plasma technology