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, the fusion community has started to develop fast surrogate models based on Machine Learning / AI models to speed up significantly the employed tools. Such tools have demonstrated to be generally applicable and
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storage, but their widespread deployment is limited by challenges in energy density, stability, solubility, and cost of electroactive redox compounds. The PhD candidate will develop and apply machine
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experimentation with AI-driven decision-making. At DIFFER, in close collaboration with our external partners, we are developing an SDL dedicated to accelerated discovery of functional energy materials. In this PhD
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