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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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requirements and focusing on data-value maximisation. This project will utilise innovative machine learning methods and tools from process systems engineering to simultaneously optimise product quality and the
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for the computer simulation of electronically excited processes in molecules and materials. In the age of net-zero it is more important than ever to obtain a deep, molecular-level understanding of the working
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