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prototype/demonstrator of a low-cost smart sensor. To develop an efficient algorithm to process the vibration signals locally and to develop the firmware to be embedded within the sensor node. To validate
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engage in immersive, simulated construction tasks, while wearable sensors monitor their physical effort, emotional states, and cognitive load. Physiological and behavioural data — including eye tracking
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, coordination, and decision-making algorithms for multiple autonomous agents—such as robots (robotic manipulators, drones, or vehicles)—that work together to achieve common goals in dynamic, uncertain
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, thermal, electromagnetic or kinetic), are critical for the sustainable operation of wireless IoT devices and remote sensors. The world can reduce reliance on batteries and fossil-fuel-derived power if more
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-time sensing, multi-sensor fusion, and intelligent algorithms can jointly enable safer, greener, and smarter rail operations. Key research topics include eco-driving, environment cooperative perception
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application from qualified women. About the position The position involves both teaching and engaging in innovative research projects on tractor autonomy, path-planning algorithms, soil compaction modeling, and
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the theory of optimization algorithms and high-dimensional statistics to address some of the most fundamental questions in ML such as the behavior of neural networks. The environment of this project is highly
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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mapping using a team of highly mobile legged or legged-wheeled robotic platforms. The research will investigate advanced algorithms for multi-robot coordination, dynamic path optimization, and collaborative