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investigations carried out on the research compressor by an integrated team of LTF and an industry partner. - Numerical modelling compressor stages and carry out simulation of methods to enhance aerodynamic
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and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods
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• Conduct statistical consultation for Helmholtz scientists and industry partners • Evaluate and apply novel statistical methods in the context of applied research • Write statistical reports on experimental
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, or learning sciences. You are interested in interdisciplinary collaboration and working in research teams. You have very good knowledge of social science research methods and statistics. You have
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research methods and statistics. - Experiences with research syntheses (e.g., meta-analyses) and video data analyses would be an advantage. - You have very good written and spoken Eng-lish skills and good
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empirical research with design practices from a comparative perspective. CUSP aims to approach urban and peripheral spatial research through ethnographic and qualitative methods. We focus on the critical
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methods (such as Machine Learning, Metric Learning, Reinforcement Learning, Graph Representation Learning, Generative Models, Domain Adaptation, etc.) for Design Automation applications. To this end, we
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parameters to understand hydraulic thermal processes, develop innovative monitoring systems, evalua-tion and implementation methods like geothermal potential assessment as well as numerical reservoir modelling
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from the perspective of flight mechanics and fluid mechanics. A sound experience in programming in C/C++ and Cuda is very desirable, the motivation to independently develop, implement and test numerical
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develop methods and software tools that aid the design of microfluidic devices (also known as Labs-on-a-Chip). While these devices are mainly designed manually thus far, we investigate methods