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fascinated by electromagnetic modeling and numerical problem solving? Do you want to contribute to the development of state-of-the-art metrology for integrated-circuit production? Information Integrated
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transparency and trade secret claims of regulated actors? And explore legal arguments in support of algorithmic transparency and data access for public interest research? How does EU law balance transparency and
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data
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well as to optimize the tooling geometry. These process simulations require efficient numerical algorithms to be practical and to enable robust optimization. Therefore, in this project you will: Develop efficient
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, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL
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, the researcher will develop theory and algorithms for (hybrid) model selection that allows to exploit domain knowledge through interactive learning. For this we will build on the minimum description length (MDL
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for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential inference algorithms as well as proofs of their correctness and efficiency
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mathematical methods, algorithms, and applications are required. Simulators are a recognized method for architectural design explorations and the implementation of software development platforms. The goal
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, employees, IT infrastructure, specialized training). Second, they may require the use of quantitative models, data analysis, and algorithms, but these applications must also safeguard the data privacy and non
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opportunity to tackle these two complementary perspectives. In the first direction, you will develop advanced system identification techniques that combine nonlinear dynamics theory with machine learning tools