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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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for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs You will be enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen. Targeted
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enrolled in the PhD program of the department of Electrical Engineering and Information Technology, RWTH Aachen. Targeted services for international employees, e.g. through our International Advisory Service
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feedback control, you will uncover fundamental connections between physical dynamics and neural network representations. We seek a highly motivated PhD candidate with an excellent master’s degree in physics
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computational models with the "exact" but lower resolution information available from experiments. Job description: Application of specially developed approaches to define for transferable force-fields with
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is using state of the art machine learning tools to extract interpretable latent dynamics. We seek a highly motivated PhD student to develop a predictive computational model using recurrent neural
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), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
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Researcher (PhD Position, m/f/d) Neural Circuits and Behavior This position is limited in accordance with § 2 WissZeitVG and § 72 HessHG, offering the opportunity for individual academic qualification and with
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 14 days ago
electrophysiology, functional imaging, or light/electron microscopy Familiarity with genetic and molecular biology tools Background in computational modeling of neural systems A demonstrated ability to secure
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1