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extrapolation based on the principle of geometric similarity and the choice of an invariant. Using this method therefore requires precise knowledge of the phenomena involved. As concerns numerical methods, while
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equations, Bayesian inference, large-scale computational methods, bioinformatics, data science, machine learning, optimisation, numerical methods. Please read more about the position and our department on our
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design scalable numerical methods for quantum master equations, implement high-performance simulations, and help build open-source tools for large-scale spin-system modeling. By improving our ability
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candidate with a strong background in some aspect of numerical analysis for PDEs and an interest in scientific machine learning and probabilistic methods, who enjoys working in collaborative inter
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could employ a range of research methods depending on their field of expertise. ABD candidates will be considered. Visa sponsorship is not available for this position. In addition, candidates relying
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Tensorflow or Pytorch is advantageous Experience in numerical methods for partial differential equations is beneficial Effective communication skills and an interest in contributing to a highly international
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“ Are you interested in the sustainable use of natural resources and would you like to research and develop new, efficient recycling methods as part of your scientific career? Then the Research Training Group
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, viscoelastic, and damage-informed formulations) under complex thermo-mechanical loading. Implementing and validating models using computational tools (e.g., numerical solvers, finite element frameworks
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related field Strong background in numerical methods and machine learning Proficiency in at least one programming language (Python, Julia, C++, …) Good analytical skills Good organizational skills and
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of multi-physics and mixed-dimensional PDEs. The goal of the research is the development and analysis of computational methods combined with model order reduction methods, including data-driven ones