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international research networks. Your future tasks: Active participation in research, teaching and administration, which involves: Researching, writing and completing a (publication-ready) habilitation thesis, a
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that aims to tackle this ambitious challenge by developing and applying new software tools that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology
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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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Networks (PINNs) perform when tackling these tough equations. We’re diving into key questions such as: How does the presence of a small parameter affect the number of training points needed? How complex does
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transformation processes and to conduct research with societal relevance. Our research group is excellently embedded in the international network of researchers and practitioners, and we strive to contribute
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applications for third-party funding + Teaching of courses as defined by the collective agreement + Supervision of trainees and students We expect the successful candidate to sign a doctoral thesis agreement
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well as independent research and independent teaching as defined by the collective agreement. The development or expansion of an independent research profile and further post-doctoral academic qualifications
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will also be expected to provide assistance in organising research events and conferences and maintaining research networks and projects. In addition, you are expected to have excellent organisational
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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