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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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in large pre-trained models (vision-language models), generative models (flow matching, diffusion), simulation-based inference, and robust and active learning. The group has a wide network of
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must be inferred from the data. The overall goal is to develop reliable and robust statistical methods that can contribute to scientific understanding and inform decision-making and public policy
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, or multitrophic interactions is a strong merit. Experience with computational methods such as multilayer networks, Bayesian inference, or higher-order network models is also a merit. The ability to communicate
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project focuses on developing a mechanistic understanding of coupled redox–dissolution pathways in multi-metal oxide systems and how these pathways can be inferred from real-time process signals. Using
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research. The ideal candidate will have experience in all of the following areas: -large scale population genomics, demographic inference, and evolutionary genomics -graph-based pangenomics and structural
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topics within applied economics. Applicants should have experience with experimental methods and/or empirical analysis of observational data with an emphasis on causal inference. Experience with methods
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simulations, density functional theory (DFT), molecular simulations, or machine-learning potentials. Experience with generative AI, active learning, uncertainty quantification, Bayesian optimization
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Railway Mechanics is the core of the competence center CHARMEC whose research focuses on the mechanical interaction between components in trains and tracks. Among other things, the research provides
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application! Work assignments The project's contribution will lie at the intersection of random matrix theory and statistical inference theory, with applications in several fields of science. Special emphasis