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of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting
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interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and
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strong background in computational condensed matter physics. Experience with first-principles electronic structure methods and scientific programming is expected. The ideal candidate is highly motivated
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to transform the entire structure and functioning of Arctic coastal marine ecosystems. CIFAR is a research center that aims to unravel how the complex interplay between ice melt, runoff and ice formation across
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position is full-time, starting 1 June 2026 (or as soon as possible thereafter), and is initially for two years. As part of the recruitment process, a shortlisting procedure will be applied. Expected start
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project partners. Extend an in-house seakeeping solver based on a time-domain boundary element method and couple it with the interior-flow solvers. Investigate complex physics associated with the coupled
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methods to decode the physics, chemistry, and biology of complex multiphase bioreactors. Working within PROSYS, you will have the freedom to design rigorous simulation campaigns, develop reduced‑order
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-year extension. The project is fully funded by the Independent Research Fund Denmark (DFF). The main objective of this project is to develop physics-constrained, data-driven turbulence models
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of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
on “Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data