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contribute to the activities including TES unit development, laboratory testing and techno-economic analysis to identify optimal integration opportunities. Cooperation with industrial and academic national and
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advancements and practical implementations optimized for modern HPC systems. The postdoc will primarily contribute to one or more of the following research areas: Development of efficient numerical linear
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algorithms to enhance the design optimization process Create predictive models using Python-based frameworks (e.g. scikit-learn, PyMC) to accelerate design iterations Integrate ML approaches with finite
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to the above requirements • Strong background in optimization and partial differential equations • Strong background in numerical mathematics and computing • Machine learning skills are welcome • English skills
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mechanics, numerical methods, microstructural mechanics, structural optimization, and experimental methods. The department also has strong activity in X-ray and neutron methods for materials research. Project
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Your Job: Development and optimization of high-temperature heat pipes for fusion applications Numerical simulations of heat transfer and fluid dynamics in heat pipes using COMSOL, ANSYS, or similar
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optimizing simulation tools such as CalPhad to support experimental findings. Conducting in-depth metallographic analysis and establishing correlations between mechanical properties and microstructural
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multiplex and multilayer networks alongside with the observed links in order to predict or reconstruct the missing links. The first step is to explore different optimization methods using low rank tensor
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machine learning—for chemical and biological applications. You will design and implement models ranging from molecular to process scales, develop model-predictive control and optimization strategies, run
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with neuroimaging, numerical mathematics, optimization, inverse problems, software development, motivation and research interests. The location for this research will be the workgroup of Prof. Dr