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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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, 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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“ 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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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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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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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
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in numerical methods and machine learning. - Research experience at the postdoctoral level, with an established record of publications and impact appropriate to the career stage. - Teaching experience
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Post-doctoral Researcher position. We seek an ambitious, well-trained, and collaborative scholar with a PhD in economics or a related field. Candidates must have expertise in experimental methods and
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, natural sciences, humanities and social sciences. Today over 19.000 students enrolled and 5500 employees work in 10 faculties. (https://www.uni-stuttgart.de/en/ ) The Institute of Space Systems is one