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garments into recycle, reuse, and manual-review streams; this PhD project tackles the core challenge of designing and optimizing a high-throughput hyperspectral imaging system, fused with complementary
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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
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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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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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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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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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, the project will enable the evaluation and optimization of resilience strate-gies. The framework will be validated through pilot studies, ensuring its applicability to real-world industrial chal-lenges
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of underrepresented groups. We wish to reflect society both in age, gender and culture, as we believe that this would optimize the dynamics in our organization. Therefore, we support and understand the need for a work
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Description We are looking for a PhD-candidate interested in topics that lie on the border of optimization by the use of heuristic algorithms and (Explainable) Artificial Intelligence ((X)AI). Specifically, in