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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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. Our research spans 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
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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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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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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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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
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, machine learning algorithms, and experimental condition-monitoring systems (accelerometers, acoustic emission sensors). WHAT WE ARE LOOKING FOR You have a strong interest in scientific and/or project-based