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with Prof. Qi (now at Georgia Institute of Technology), this field has received significant advancements, with numerous proofs-of-concept showcasing diverse behaviors and materials for potential future
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the initial phase, you will develop and optimize physical and numerical models describing the electron optics of the complete probe-forming column, including the multi-beam generation unit, imaging lenses
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three main phases. In the initial phase, you will develop and optimize physical and numerical models describing the electron optics of the complete probe-forming column, including the multi-beam
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energy management, optimal configuration, a combination of different energy sources, etc. Research goals will be achieved by proper verification of the model. Applicants should fulfill the following
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environmental footprint.In this context, the optimization of material recovery from end-of-life vehicle scrap is challenged not only by the heterogeneous and evolving nature of this scrap, but also by the diverse
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are to: Develop a probabilistic machine learning tool that can determine the optimal grinding parameters for different scenarios based on required material removal depth and rail grade. Generate data through
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how very different mutations can trigger similar developmental problems. More information can be found on our homepage . Project Summary This PhD project builds upon an ongoing collaboration between
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-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 the microscale up
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specifically naval architecture, generative AI is lagging behind. This is largely due to data scarcity in the maritime domain. Unlike media domains where vast datasets are readily available, ship design data is
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specifically naval architecture, generative AI is lagging behind. This is largely due to data scarcity in the maritime domain. Unlike media domains where vast datasets are readily available, ship design data is