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project, you will develop machine learning models that learn from high-throughput experimental datasets to uncover structure–property relationships and guide the selection of new experiments. The datasets
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to create strong business cases, where carbon is no longer a costly liability but a valuable feedstock for sustainable fuels and chemicals. Based on these ambitions, the market for CO₂ capture and conversion
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to create strong business cases, where carbon is no longer a costly liability but a valuable feedstock for sustainable fuels and chemicals. Based on these ambitions, the market for CO₂ capture and conversion
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publications presentations at conferences, and stakeholder meetings. Complete a PhD thesis based on the research within four years. Requirements Fundamental physics background and/or electrical engineering