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their glial niche. Genetically encoded, fluorescent metabolite sensors will be used to study the underlying metabolite dynamics. The work will also incorporate various molecular biology techniques, as
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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- looking. Sensor technology plays a key role in this transformation, enabling real-time monitoring, automation, and intelligent decision-making. Despite these needs, many water treatment processes still rely
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meteorological stations and occasionally supplemented by opportunistic sensors. The integration of diverse opportunistic sensor data sources [4], such as private weather stations, further broadens the capability
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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the environment, including traffic conditions, travel time, and cost. The project will define the DRL components (states, actions, rewards, policies), select and implement suitable DRL algorithms, and integrate
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, C/C++ and/or Java, etc.; experience with the implementation of specialized transport modelling software, optimization algorithms and procedures; strong ability and desire to learn new programming
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prediction of queue dissolution by combining traffic flow theory with data from roadway and AMOD sensors, nonlinear optimization of the signal plan, cooperative control of traffic signals and AMOD vehicle
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the future of advanced air mobility through involvement in innovative drone-related projects that bridge technology, urban planning, material sciences, sensors and aviation. With the upcoming Smart Mobility
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. Such multimodal energy sources will become increasingly vital over the next decades, not only as sources of renewable energy but also for high-tech applications, such as powering unattended wireless sensors