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at the interface of data science and the scientific domains pursued at the three participating Helmholtz centers. Methodologically, a broad range of topics is covered, from large-scale data management to data mining
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of Prof. Dr. Frank Cichos and Dr. Nico Scherf (Max Planck Institute for Human Cognition and Brain Sciences). The position is part of a collaborative project in the Center for Scalable Data Analytics and
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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of neural hydrology, where hydrological models are directly learned from data via machine learning (e.g., LSTM neural networks, [1]). Initially, these models ignored all physical background knowledge and did
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Research Center (CRC) “Data-driven agile planning for responsible mobility” (AgiMo), funded by the German Research Foundation (DFG). This interdisciplinary center, involving four universities and the German
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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Research Center (CRC) “Data-driven agile planning for responsible mobility” (AgiMo), funded by the German Research Foundation (DFG). This interdisciplinary center, involving four universities and the German
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activities of the Schlundt lab here . We are excellently embedded in a large research infrastructure and with the neighboring departments as well as one of Germany’s best university hospitals. The full job
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machine‑learning or data‑analytics tools High‑level programming skills (Python, R, Julia) to build, test, and optimize models of geochemical systems Interest in large‑scale computational simulations (e.g
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, CTR) Design and implement physics-informed deep neural network architectures which accelerate finding electron bunch shapes self-consistently for large measurement campaigns Implementation and