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(terrestrial and NTN). The goal of this research is to design and develop algorithms and techniques that adapt to the environment, minimizing signaling overhead associated with channel estimation and enhancing
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comprehensive capabilities in conducting sophisticated fluid dynamics data analysis. This job includes aerospace-related machine learning algorithms. Specific duties include data analysis from wind tunnel
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methods to improve the deployment, adaptation capabilities and safety of robots and critical infrastructures. The developed algorithms will be evaluated on legged robots, wheel-based robots and under
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a Research Engineer to help us develop the next generation of autonomous systems and algorithms for Earth and space applications. Our group doesn't just develop new theories, but puts them to the test
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. The focus is on developing AI-supported, sensor-based solutions for real-time monitoring and optimization of manufacturing processes. These solutions aim to assess process conditions during production
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research team. Key research areas include: Development of low-carbon materials and tunable thermal energy storage materials integrated with smart sensors and advanced algorithms Creation of Digital Twins
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sensor and IoT systems deployed on unmanned robots and drones, these algorithms are tailored for rapid response scenarios, such as the release of biological, chemical, or radiological hazardous substances
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sensor and IoT systems deployed on unmanned robots and drones, these algorithms are tailored for rapid response scenarios, such as the release of biological, chemical, or radiological hazardous substances
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, but also in traffic monitoring or in the media context, for example when it comes to automatic metadata extraction and audio manipulation detection. Another focus is the development of algorithms
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on the use of Machine Learning algorithms for rapid damage assessment. Research topics could focus on: the definition and use of novel damage sensitive features, physics informed machine learning, transfer