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apply machine learning and optimization algorithms in order to achieve the design of such nanophotonic structures. As a postdoc you will be part of the Condensed Matter and Materials Theory division, a
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optimization in robotics, electromobility, and autonomous driving. The team is international and combines expertise in control, optimization, and statistical inference. A key strength is the close collaboration
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Do you dream of organic and holistic ways of automating the design, motion optimization and control of legged robots? With this postdoc position, you have the opportunity to be a part of the ongoing
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inverse design, optimization, and/or machine learning for designing and optimizing devices in integrated photonics, which will be subsequently fabricated and tested by our experimental partners in Metapix
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—knowledge essential for designing new materials with properties optimized for specific technologies. The main research tools will include advanced neutron spectroscopy at facilities such as the ISIS Neutron
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to maintain system stability during major disturbances (e.g., generator outages, transmission line failures, or system separations) Developing methodologies for optimally allocating these services based
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characterization of algal food protein ingredients, ensuring high yield and quality. The latter will be optimized from several aspects, including e.g., in vitro nutrient digestibility and volatile compound profile
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yield and quality. The latter will be optimized from several aspects, including e.g., in vitro nutrient digestibility and volatile compound profile. The work will be carried out in close collaboration
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on III-nitride devices and circuits for both high frequency and power applications. We will explore new concepts in III-nitride semiconductor material and device processing to optimize different important
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), enabling 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