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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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proficiency in English a structured, self-driven, independent approach to technical work and good collaboration skills coursework or other experiences in the following subjects are valued: optimization, linear
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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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of ion conductivity in complex battery materials on a large scale. Model‑generated data will be used to identify key relationships between material structure and ionic conductivity through advanced data
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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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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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student to join the Structural Chemistry research programme at the Department of Chemistry-Ångström Laboratory, commencing in 2026. The successful candidate will join an interdisciplinary project focused
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innovative approaches to design and optimize materials with enhanced bioactivity. Experience in design and synthesis of peptides will be considered a strong merit, as well as expertise in chemical modification