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11.11.2024, Wissenschaftliches Personal In the project “BIG-ROHU” (BIG Data - Rotor Health and Usage Monitoring), a system is being developed which provides information on both the health and the actual stress of helicopter components using a data-based as well as a physics-based approach. In...
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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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applied to control problems or tiny RL scenarios. Explore digital hardware realizations of the proposed RL algorithms within existing spiking neural network chip designs. Quantitative comparisons with
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-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will be done in close collaboration with the other members of the FADOS network. Further information
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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The project will include two secondments (6 months each): Stuttgart University (USTUTT): formulation of n- and p-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will
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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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. Through academic, clinical, and industry partnerships, as well as global networks, we strive to translate biological discoveries into applications that enable the early detection of deviations from health
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to understand, predict, and treat diseases. You will work with multimodal biomedical datasets including omics, imaging, and patient data and apply cutting-edge AI models such as graph neural networks, transformer
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multi-electrode arrays to evaluate the activity of neural network formation Testing the inter-laboratory reproducibility of the model between the BfR, Berlin, and the TiHo, Hannover Preparation