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to research on some of the following themes: New algorithms for parallel/distributed AI/ML Hardware-aware and resource-efficient partitioning for parallel/distributed AI/ML Optimization of process-to-process
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that integrate prediction and control algorithms, optimizing data transformations, offloading and distributed computing, and exploiting mechanisms such as network slicing and multi-access edge computing
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and targeted, computationally expensive, model simulations. This experimental design process is envisioned to update iteratively as new data become available to optimally infer surface fluxes across
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. The goal of the PhD will be to seek mechanistic insight into the electrode polarization processes as well as strategies for improving performance by optimization of composition, microstructure, and the
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physical latency limits and human perceptual tolerances. The work will comprise designing networking and computing architectures that integrate prediction and control algorithms, optimizing data
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well as strategies for improving performance by optimization of composition, microstructure, and the interface to the proton-conducting electrolyte. The content of the PhD Research Fellowship can be tailored according