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methods, image processing, high-speed imaging, cryogenic testing, composites, numerical modelling or non-destructive testing is an advantage. You can work independently while collaborating effectively with
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modeling, construction of numerical methods, coding, testing, numerical simulations, and possibly measurements. You will mainly do your programming work in a mixed programming environment, i.e. combining
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modeling Demonstrated experience with mathematical modelling, and an ability to learn new concepts and (computational) methods as needed A demonstrated ability to work with programming languages such as R
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, Physics, Computational Science, or a closely related field. A strong background in numerical methods, scientific computing, computational physics, or quantum computing. Excellent programming skills in
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scientific knowledge, such as physical laws, differential equations, and domain-specific constraints, to model, simulate, and understand complex systems. The project will explore modern SciML methods
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will be combined with theoretical modelling, statistical data analysis, and numerical simulations to identify the governing mechanisms and to determine whether and how proximity to a phase transition
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mathematics, numerical oriented geosciences, or related field with strong quantitative focus; Strong background in machine learning methods such as neural networks and transformers; Knowledge on handling large
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to increasing traffic loads and environmental effects, efficient methods for monitoring their condition become essential. Traditional inspection approaches can be costly, time-consuming, and limited in spatial
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use, and long-term crop management goals. While several algorithms have shown promising results in energy savings and crop yield, most of these methods have only been tested in simulation, and make use
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, reliability, and resilience of rail transport. As these assets age and are exposed to increasing traffic loads and environmental effects, efficient methods for monitoring their condition become essential