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of state Developing a semi-primitive approach for the electrostatic forces Predicting solid-liquid equilibria (3) Molecular thermodynamics for inhomogeneous electrolyte systems Developing classical density
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, international group of 13 researchers from 8 countries, with expertise across energy systems and markets, optimization, control, game theory, and machine learning. Interdisciplinary by design: Work at the
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, large-scale foundation models will be developed and trained on the Aalborg Supercomputer (TAAURUS), facilitating advanced ECG representation learning and prediction of acute coronary events in
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be applied to core technologies of the department, e.g., batteries and catalysts. The project includes collaboration with experimentalists at DTU, who will verify the computational predictions as
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to apply machine learning techniques to a combination of experimental data and simulation results, aiming for faster and more accurate predictions. About Us You will join an international and highly
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on these predictions, you will help create glass-ceramic materials with carefully controlled crystalline regions and well-designed glass/crystal interfaces that promote fast-ion pathways. The most promising material
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ongoing national and international research projects and will focus on one or more of the following themes: Artificial intelligence and data driven methods for energy system analysis and control Data-driven
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exciton–exciton interactions in semiconducting 2D materials such as MoTe₂. Using ultrafast spectroscopy on EDC cavities coupled to excitonic layers, we will test theoretical predictions of single-photon
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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