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-edge solutions and pushing the boundaries in the field Develop advanced artificial neural networks (ANN), including training, mapping, and weight quantization Collaborate with cross-functional teams
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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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. 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
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degree in computational engineering, mechanical engineering, computer science, applied mathematics, physics or a similar area very good programming skills in Python good prior experience with neural
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., neural networks, Gaussian processes, active learning) interest in materials science (e.g., SCC) excellent knowledge of English (written and spoken) high degree of motivation, creativity, and flexibility
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architectures and principles from Bayesian neural networks and biological sequence models, including large DNA and protein language models. The project also aims to develop a prototype federated learning
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programming and know how to use version control. ▪ You are experienced in the usage of machine learning (e.g., Actor-critic algorithms, deep neural networks, support vector machines, unsupervised learning