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around us? At Maastricht University, you will investigate how individuals differ in predictive processing by combining behavioural and neural testing with computational modelling. Together with colleagues
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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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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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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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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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. Its primary objective is to guide cities in their transition toward net-zero transport emissions by reallocating urban space, redesigning infrastructure, and optimizing the movement of people and goods
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those in a data- and compute-efficient manner to various problem settings. However, the perceptual possibilities are critically hindered by the language-focussed optimization of current foundation models
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are critically hindered by the language-focussed optimization of current foundation models, fundamentally limiting their ability for spatial reasoning, temporal logic, and operating in low-resource scenarios
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in synthetic cells 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
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affordable and durable long-duration energy storage. The approach is to use hierarchical structures, i.e. complex material layers that can be optimized to specific battery chemistries and flow phenomena from