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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
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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
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systems. This PhD project, part of a national initiative, aims to use AI to design and optimize thermal interface materials (TIMs). It combines machine learning, materials informatics, and experiments
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of numerical modelling (e.g. CFD, FEA, FSI, optimization, ML), but we are also involved in experiments and real-life monitoring to support our findings. Besides research, our division is actively involved in
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central role in streamlining and standardizing the design flow for quantum device fabrication. This includes implementing and improving design rule checks (DRC), optimizing and debugging code, and
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having resided in the country of the host institution for more than 12 months in the last 36 months). Mandatory: Master’s degree in Systems Engineering, Control, Optimization, or related field. Strong