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several related areas of expertise: generative AI for robotic planning and control, cognitive control, distributed perception and control, and HW-SW co-design of algorithms. Our research is applied across
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, in tropical rainforests of Latin America You will assist in the teaching activities at the department, specifically in the domain of systematic and evolutionary botany You will assist in the internal
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functionality. Your work will involve designing advanced algorithms that efficiently utilize UWB signal features (RSSI, channel impulse response, phase and amplitude data, Doppler maps,..) to support both high
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machine. We develop quasi-Newton coupling algorithms for partitioned simulation of FSI, and we solve challenging FSI problems in the energy transition and in industry. This research is often in
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machine. We develop quasi-Newton coupling algorithms for partitioned simulation of FSI, and we solve challenging FSI problems in the energy transition and in industry. This research is often in
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to ensure reusability and scalability. To achieve this, you will work with high-fidelity simulation tools, machine learning algorithms, and experimental condition-monitoring systems (accelerometers, acoustic
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algorithms need to be developed to determine the winners of the auction, as well as intermediate feedback that can be communicated to the bidders, to facilitate the bidding process. The market-based research
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quasi-Newton coupling algorithms for partitioned simulation of FSI, and we solve challenging FSI problems in the energy transition and in industry. This research is often in collaboration with another
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will design, implement, and validate novel algorithms, and benchmark them against state-of-the-art reconstruction pipelines. Strong programming skills (e.g., Python/C++ and GPU-based computing) and