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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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of these materials.State-of-the-art characterization techniques such as DSC, DMA, DTMA, micro-Computed Tomography (micro-CT), optical microscopy and Scanning Electron Microscopy (SEM) are combined with advanced numerical
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with privacy by developing techniques that optimize both aspects. The candidate will perform the work together with a team of postdoctoral researchers who are experts on the field and other PhD student
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for wind turbines, with the ultimate objective of including structural health information in windfarm asset management to optimise structural lifetime consumption while guaranteeing optimal power production
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monitoring of different sources of N fluxes in the system. He/she will need to contribute to the optimization and implementation of advanced sensing platforms for the collection of data on soil N, more