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functional theory for electrolyte systems Predicting interfacial tension of electrolyte systems Benchmarking electrolyte thermodynamic modelling approaches We are looking for academic excellence, and we expect
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom
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systems or smart buildings, such as regression, classification, time series analysis, or basic predictive modelling. Experience with data handling, including data cleaning, transformation, exploratory
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structures obtained via Dark-Field X-ray Microscopy (a synchrotron-based imaging technique), combined with phase field modeling predictions of the structural evolution. Qualifications: Background in data
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potentials to interpret experimental data and predict catalytic performance. The tasks can include: Advancing equivariant neural network potentials (ENNPs) to model nanoparticle energy surfaces. Building atom
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interfaces This project explores advanced inverse design methods for next-generation EDC photonic systems. By combining topology optimization with physics-based modeling, it aims to discover new light–matter