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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
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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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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
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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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research, driving the development of innovative, sustainable and smart processes and products through world-class research while actively investing in graduate and undergraduate education and mentorship
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of the practitioner. Hence, there is a strong need for a human-centered approach that enables experts to inform and guide the learning process. The second reason is that interpretable models typically do not achieve