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methods, Machine Learning algorithms, and prototypical Energy Management systems (EMS) controlling complex energy systems like buildings, electricity distribution grids and thermal energy systems for a
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reconstruction to overcome these challenges. Your tasks - develop physics-informed, self-supervised learning approaches for phase retrieval - implement reconstruction algorithms on HPC clusters for large-scale
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tomography and local adaptive reconstruction to overcome these challenges. Your tasks develop physics-informed, self-supervised learning approaches for phase retrieval implement reconstruction algorithms
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(4DSTEM). This approach will combine three-dimensional charge distribution data, generated through atomistic simulations, with machine-learning-driven modelling to guide and refine the phase reconstruction
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classification algorithms need to be extended with the capability to detect out-of-distribution environments and to autonomously infer their traversability. In this thesis, you will design a semantic
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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collaborations and spin-offs. With around 1,300 employees, we conduct research in four main areas: energy supply, energy distribution, energy storage and energy use. The state-of-the-art R&D infrastructure
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. Hoffmann’s research group, an innovative project is being conducted on the processing and analysis of seismic data from a large-scale Distributed Acoustic Sensing (DAS) network. This network is installed
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are as important as high accuracy. Supervise student projects at BSc, MSc, and PhD level. Work with experts at the Jülich Supercomputing Centre (JSC) to run your algorithms/tools on large distributed
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, reliability, and consistent behavior. Learning-based controllers can achieve high performance in complex and uncertain environments, yet ensuring predictable operation under distribution shifts, sensor noise