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in materials theory. The Division of materials theory offers world-class research in solid-state theory, using both numerical and analytical methods. The research is focused on magnetism, electronic
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of the system is still insufficiently understood. This project investigates the underlying fluid-structure interaction mechanisms and develops advanced numerical methods for high-fidelity simulation
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learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron Collider, with the support of competent and friendly colleagues in
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numerical simulations to understand molecular mechanisms of epigenetic regulation by the Polycomb and Trithorax group proteins. Project description Chromosomal rearrangements of the human MLL1 gene
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supercomputing facilities. Knowledge of numerical methods for large-scale simulations of fluid flow, heat transfer, and chemically reacting systems. Documented ability to work effectively in interdisciplinary
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postdoctoral researcher with a solid background in one or more of the following areas: game theory, optimization algorithms, and numerical methods. The successful candidate will develop efficient algorithms and
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. Plant science/ecology, especially related to forest ecosystems. Computer programming. Data analysis (machine learning, statistics, numerical analysis, time-series analysis, etc.). Quantitative methods in
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, modelling, fabrication, prototyping, measurements or experimental validation. Additional merits include machine learning, optimization, signal processing, numerical acoustic simulation (for example FEM/BEM
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, use imitation learning algorithms to learn pick-and-place actions, design HRI experiments with users, evaluate data, and share the code and benchmarks in open repositories. This postdoctoral position is
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in day-to-day work. Strong computational and numerical modelling skills, including finite element or multiphysics simulation and scientific programming. Awareness of diversity and equal treatment