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particular, we aim to develop a neural network architecture that will allow us to accelerate solving AC power flow (AC-PF) computations, potentially facilitating real‑time contingency analysis, rapid design
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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based techniques to analyse spike train recordings to advance our understanding of neural population coding while maintaining clarity in the interpretation of results. Concurrently, AI-based methods
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Chromatography modeling, while crucial for modern bipporcess development, still
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Develop methods and workflows to construct robust co-regulation networks from large
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will be developed using the example of the novel Earth Explorer EarthCARE and will be integrated as observation operator to the sophisticated data assimilation system of the EURopean Air pollution
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models, which are essential for understanding climate change impacts. The work involves reviewing existing modeling and model–data fusion techniques, and developing faster, machine-learning–based tools
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Infrastructure? No Offer Description Work group: JSC - Jülich Supercomputing Centre Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student to develop learning-based surrogate
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staff position within a Research Infrastructure? No Offer Description 2 Doctoral Researchers (f/m/d) in Computational and Data Science at KIT https://www.kcds.kit.edu/72.php Application deadline: January
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Infrastructure? No Offer Description Work group: JSC - Jülich Supercomputing Centre Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student to contribute to the development of fast