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to conduct research and teach in an internationally networked team while developing your own academic profile. The contract is for a period of three years. Initially limited to 1.5 years, the employment
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machine learning. The goal of this research project is to investigate how far standard proofs in numerical analysis and approximation theory can be automated by a (neural network) guided search over
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at the Forschungszentrum Jülich in Germany. The PhD Project is partially funded by the EU Doctoral Network QLUSTER (HORIZON-MSCA-DN-2021, 101072964). Please note that, according to the mobility rules established by the EC
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to engage in interdisciplinary exchange and research work in teams • Willingness to engage in network building Desirable are: • Experience in project management or project coordination • Field
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international partners and networks and become familiar with initiatives similar to the ECH. You develop metrics for success and benchmarking frameworks to monitor and optimize all Hub programs and performance in
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learning and deep neural networks and will be able to take part in research project related to the above. The start of the contract is the 1st of September 2025 or later, the exact starting date is
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neural networks. The key challenge? Designing robust and stable numerical schemes that remain efficient even in high dimensions, effectively pushing back against the curse of dimensionality. The ideal
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. The goal is to design innovative assimilation schemes using nonlinear approximation tools—such as neural networks, spline functions, or Gaussian random fields. The core challenge? Developing methods