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, machine learning and statistical methods as part of a close collaboration between Uppsala University, Westinghouse, and Vattenfall? Would you like to contribute to the development of fast, robust and
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chemistry, molecular modelling, and artificial intelligence. A central part of the work is to develop and apply modern AI and machine learning methods to explore, understand, and optimize high-entropy
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evolving kinetic regimes. The work will combine experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process
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, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine
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of the same underlying process, traditionally interpreted manually and in isolation. Multimodal machine learning offers a principled alternative: integrating heterogeneous experimental and simulation
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flow and transport, fractured media, uncertainty quantification, statistical methods, or machine learning is considered a merit. Rules governing PhD students are set out in the Higher Education
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is an emerging research field that uses machine learning, data analysis and computational modelling to guide the discovery, characterization and optimization of new materials. By accelerating
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advanced data‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the