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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real
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Description Are you fascinated by the incredible capabilities of neural networks? Are you interested in applying theoretical methods to understand computational efficiency in neural systems? If so, come and
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? We invite applications for a PhD position, focusing on the design and implementation of Spiking Neural Networks (SNNs) using CMOS technology. Project Overview This PhD position is part of a
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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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that explicitly incorporates protein–ligand dynamics. You will be responsible for: Designing and implementing innovative deep neural network models. Integrating physical principles and molecular modeling knowledge
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and an acute risk of brain damage by haemorrhage and deep brain bleeding. In BRAINET(Networked Distributed Neural Interfaces for Interference-Based Brain Stimulation), we confront the pressing need
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and an acute risk of brain damage by haemorrhage and deep brain bleeding. In BRAINET (Networked Distributed Neural Interfaces for Interference-Based Brain Stimulation), we confront the pressing need
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implementations (e.g., biophysical models), as well as models of machine intelligence (e.g., deep convolutional neural networks). We test the models' predictions in our empirical studies with human participants