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-driven model selection, and deep learning for data analysis and feature extraction from characterisation data. Surrogate modelling will be employed to reduce computational costs, and AI-based uncertainty
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in the groups of Dr Florence Hardy and Prof Anthony Green, University of Manchester, as part of the cross-institutional BioAID Doctoral Training Programme, including world-leading experts from Queen's
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using existing NPL datasets. The work will integrate suitable physics-based models (for example PV performance modelling, electro-thermal and thermofluid dynamics) with deep learning and multi-fidelity
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Pharmaceutical Roundtable. In this project we will employ deep learning-based protein sequence design tools to deliver biocatalysts for peptide synthesis. These designed enzymes will be further optimized using