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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data
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, such as graphene-like materials, has brought a fresh stimulus for spintronics. This project aims to understand, observe, and realize novel charge, spin, and orbital phenomena using thin film
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are looking for an engaged, enthusiastic, and curious person who is passionate about understanding, forecasting, and mitigating the impacts of climate change on biodiversity. We particularly welcome researchers
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refinement (TOPAS, GSAS-II, FullProf), ideally total scattering and pair distribution function analysis. Molecular conductors, radical and charge-transfer salts, or oxocarbon electrode chemistry. Synchrotron
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conditions, including charging protocols, temperature and mechanical loading, influence battery performance, degradation and lifetime. The work combines physics-based modelling with machine learning
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to unforeseen stress D. Multitrophic interaction networks as drivers of competitor coexistence under global change E. From training load to adaptation: Modeling of stress, resilience, and performance in elite
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decides whether the cell works: unlike a liquid electrolyte, a solid cell must build and maintain every solid-solid contact under load, while ion insertion and extraction cause swelling and shrinkage and
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charge for use in energy, internet-of-things, health care and biology applications. Research topics include synthesis, material science, theory and modeling, device physics, nanotechnology, biotechnology