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consumption while guaranteeing optimal power production. You will work on the cutting edge of both wind energy and machine learning, two of the fastest growing scientific disciplines, to develop graph-based
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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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part of the NWO OTP grant “AMVibe:Vibration-Based Non-Destructive Testing for Metal Additive Manufacturing”. The PhD researcher will be supervised by dr. L. (Liangliang) Cheng, and co-supervised by Prof
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. (Liangliang) Cheng, and co-supervised by Prof. M. (Maryam) GhandchiTehrani from Dynamics and Vibration Group, and co-supervised by Prof. Y. (Yutao) Pei from Advanced Production Engineering Group, and also work
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of light in air than in glass), lower nonlinearity and dispersion, and the liberty of choosing light wavelengths in a wider spectral range. These HCFs are based on anti-resonant reflection by sub-micron
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is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture. The ongoing societal transformation and large green investments in
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to: - Developing underwater communication systems using deep learning which are well-performing to nonlinear channels. - Establishing a deep learning architecture which is optimal for underwater acoustic
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Smartlife, leveraging model and data-driven digital twins for smart asset management and lifetime optimization of offshore windfarms. For this function, our Brussels Humanities, Sciences & Engineering Campus
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, feedback optimization, distributed control, consensus protocols, nonlinear control, robust control, with application to energy systems (e.g. smart grids, district heating, hydrogen networks) and traffic