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data analysis and develop sophisticated mathematical models for simulating power system behaviors under various scenarios. Development and Testing: Design and develop control algorithms to enhance grid
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on the design 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
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Computational Molecular Medicine, led by Prof Julien Gagneur, develops computational approaches to study the genetic basis of gene regulation and its implication in diseases. Applications of our work range from
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energy use more efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a
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focus on developing general methods and, then, apply them on fields where their performance overcomes the state of the art. In an upcoming project together with an industrial partner , we aim to establish
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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
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efficient. We develop new optimization methods, machine learning algorithms, and prototypical systems controlling complex energy systems like electric grids and thermal systems for a sustainable future. These
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for various technologies and develop algorithms and software tools dedicated to accelerating research on multiple levels. We are working at the intersection of computer science, physics, and material science to
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Smart Cities Applications at the soonest possible date. About us The Professorship of Data Science in Earth Observation (Prof. Xiaoxiang Zhu) develops modern signal processing and AI solutions to extract
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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