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to charge carriers in single-component organic solar cell materials and how a temperature gradient is converted to electricity in organic thermoelectrics. An important practical aim of these simulations is to
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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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potentially druggable targets. Depending on interest, the student will have an opportunity to contribute to other projects within the team and learn a range of important techniques such as cellular, animal