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Field
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-Brain inspired Neuromorphic Nanophotonics (InsectNeuroNano). The long-term vision of this project is a novel on-chip hybrid nanostructure platform for energy-efficient, fast artificial neural networks and
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Carl von Ossietzky Universität Oldenburg | Oldenburg Oldenburg, Niedersachsen | Germany | about 2 hours ago
physiological insect navigation data. Modeling navigation neural circuits. Close collaboration with robotics teams. Collaborating with other researchers on interdisciplinary research projects and publications
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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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of Higher Education and Research (MESR). PINNACLE: Physics-Informed Neural Networks for Accelerated Cloud Light-Scattering Emulation Artificial intelligence is profoundly transforming atmospheric
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neuroscience and data analysis Proficiency in programming (e.g., Python, MATLAB, and similar languages) Experience with large-scale neural network simulations Experience with analysing large-scale neural
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respect to an infinitesimal perturbation of the dataset, provide a rigorous framework to: - **Identify the most informative samples** among the predictions of a deep neural network (DNN), with the goal
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of this project is a novel on-chip hybrid nanostructure platform for energy-efficient, fast artificial neural networks and integrated sensor arrays. The project combines several different concepts: Progress in
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seeking a highly motivated PhD student to join our team to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the candidate will be
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seeking a highly motivated PhD student to join our team to work on the design and implementation of Oscillatory Neural Networks (ONNs) for physics-based computing applications. You as the successful
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-film solar cells.” You will become part of an enthusiastic team working closely with collaborators at DTU Physics and DTU Nanolab to advance neural network-based methods for materials discovery. Project