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increasingly rely on data-driven models to extract, represent, and interpret information from complex and evolving environments. Traditional machine learning approaches, as well as many classical signal
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probabilistic forward model (a digital twin) that maps microstructure to electrochemical performance. This involves simulation-based inference and physics-informed machine learning techniques that can quantify
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to influence chemical reactions Chemical system simulation or thermodynamic/speciation modeling (e.g., HSC Chemistry, FactSage, or similar) Characterization of materials using techniques such as ICP-OES/ICP-MS
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, comparing it with other drug classes and countries. Applying a mixed-method approach, the project integrates qualitative case studies with quantitative Social Network Analysis, simulations (Agent-Based Models