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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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directions of his/her own choice. The successful candidate will be a creative scientist who enjoys both writing and documenting model source code, as well as seeking novel model applications. As a result of
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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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a fair and equitable work environment, where diversity is an asset and individuals can flourish. It is that easy to apply: Scientific curriculum vitae, including links to scholarly profile and code
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in the context of the successful completion of your thesis. Requirements: You are enrolled in the doctoral programme of DSPL45 and a doctoral student member of VISESS who agreed to the Code of Good
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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