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)physics, chemistry, or a related field. A solid knowledge of the theory of molecular systems and statistical mechanics, as well as skills in molecular dynamics simulations, is highly desirable. Proficiency
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to explain biological phenomena and disease mechanisms by leveraging biophysical theory and mechanistic, mathematical modeling. Our interests include the inflammatory responses to infection, the organization
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software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via
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scientists at six institutions in the Ruhr area covering experimental chemistry, theory and chemical engineering investigate how solvents are involved in the control, mediation and regulation of chemical
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substrates while advancing our understanding of deep learning through dynamical systems theory. You will work with two cutting-edge experimental systems: (1) light-controlled active particle ensembles
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demands. To break this bottleneck and cut simulation time by orders of magnitude, you will design and implement surrogate models that learn the behavior of full‑physics codes using modern machine‑learning
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welcome applications from candidates with disabilities. If multiple candidates prove to be equally qualified, those with disabilities or with equivalent status pursuant to the German Social Code IX (SGB IX
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position is to be filled at the institute at the earliest possible date, for a fixed term of three years, as part of the DFG funded project “A modular prion-like domain code for the activation of the heat
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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. Methods to be used Fieldwork: participate in one survey to understand the ecosystem and measurement techniques. C++ coding with DuMux source code and its shallow-water module (link Darcy/Richards models