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Field
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high capital expenditure and low operational costs. Therefore, an installed capacity tailored at peak demand is not cost effective, while extending the annual operation period is advantageous for meeting
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extending the annual operation period is advantageous for meeting energy needs, reducing levelised cost of energy (LCOE) and decarbonisation. Optimal utilisation of sustainable heat requires storing large
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through optimization of ion channels incorporation and activity in lipid bilayers. The project sits at the interface of biophysics, engineering and biochemistry. The PhD student will be part of
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control theory? Are you interested in working with next generation high-tech systems? The main challenge lies in the reliable operation of the entire system, where many interconnected modules affect each
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to the timing of events and the results from performance optimization need to be included in the models. This PhD position will address these challenges by developing new timing-aware distributed supervisory
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distributionally robust chance constrained optimization. Second, you will explore how data-driven models capturing the state-of-health and degradation can be integrated in the battery model. You will develop
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first focus on hedging decisions with respect to the uncertainty on the battery model itself. To this end, you will explore concepts such as distributionally robust chance constrained optimization. Second
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that can self-learn bulk visco-elastic properties? How to structure such materials to learn continually and counteract the aging of their own parts? Can we optimize self-learning materials to achieve
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Do you have a background in deep learning and computer vision? Are you
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Do you have a background in deep learning and computer vision? Are you independent, creative and eager to take initiatives? Do you enjoy working in an international research group and interacting