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investigate and develop innovative memory solutions in advanced CMOS technologies such as FDSOI. The development of integrated circuits also plays an important role in making these networked devices and their
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Description The Institute for Software and Systems Engineering (ISSE) of the University of Technologies Clausthal is hiring a academic researcher in the field of stakeholder networking & living lab
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Networks project. Planned research will investigate the characterization and recovery of bionanoparticles for vaccine delivery and gene therapy. Overall, 14 Doctoral Candidates will be trained at seven
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Master-Thesis: Machine Learning-based Heating Curves for Heat Producers in District Heating Networks
address the challenges posed by global warming. In this context, district heating networks (DHNs) play a major role since they can realize fully decarbonized heat supply utilizing various distributed heat
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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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, check your computer’s network connection. If your computer or network is protected by a firewall or proxy, make sure that Firefox is permitted to access the web. You can continue with your default DNS
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PhD/Postdoc position in trustworthy data-driven control and networked AI for rehabilitation robotics
of future communication networks”. TU Dresden and the Technical University of Munich have joined forces to form the 6G-life research hub in order to drive cutting-edge research for future 6G communication
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. Become a part of our team and join us on our journey of research and innovation! What
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-time data acquisition during production, consulting, and prototype manufacturing. Graph neural networks provide an opportunity to operate on Mesh structured data utilized in Finite Element Method (FEM
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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