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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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the possibility of an extension. TASKS: Mathematical modeling and development of inverse methods (e.g. Bayesian inversion, optimization based methods, sparsity promoting methods based on L1-norm minimization and
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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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be achieved, for example, by developing learning algorithms and bringing together different sensor systems in the vehicle and on the road. Where you put the focus - that is up to you. The concepts
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05.04.2023, Wissenschaftliches Personal We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated
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05.04.2023, Wissenschaftliches Personal We are the Autonomous Vehicles Systems (AVS) Lab and are interested in the algorithmic foundations of path and behaviour planning, control and automated
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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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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
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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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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