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Field
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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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inference and natural language processing. A central challenge when drawing causal conclusions from observational data is adjusting for confounding factors. In the social sciences, many of these factors
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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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subject area. The department has education assignments in engineering programs and master's programs. More information is available on our website . (https://kemi.uu.se/angstrom/?languageId=1 ). Work duties The
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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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whole-genome duplication across diverse plant systems (see https://www.yantlab.net/ ). The project is funded through a Formas grant aimed at restoring European ash (Fraxinus excelsior) populations
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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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understanding: detection of objects and relations between objects, and use of these relations to infer new knowledge (i.e. reasoning); (ii) explore object affordances, learn the consequences of the actions