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their pandemic potential and classification as bioweapons. This project aims to develop a machine learning-accelerated NMR platform for the discovery of high-affinity inhibitors targeting viral RNAPs. Building
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. Interested candidates may want to take a look at our recent work on machine learning molecular dynamics: https://www.nature.com/articles/s41467-024-52491-3 Project 2: Non-adiabatic Molecular Dynamics
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, please send the following materials to Gemma Ludbrook (g.ludbrook@ucl.ac.uk ): Your CV (max 2 pages), Transcripts from your previous qualifications, A letter of support from (this would ideally be from
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learning and machine learning for biological data Sequence and structure analysis of large-scale datasets Functional annotation and evolutionary analysis Collaborative research with experimental virology
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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
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-scale metagenomic assembly and genome recovery • Comparative genomics and molecular evolution • Machine-learning-based protein prediction • Data integration, bioinformatics and phylogenetics • Scientific
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interest are also welcome. Motivation to learn about nanoparticle formulation, microfluidics, or data analysis is highly valued. Motivation to work in AMR and nanomedicine and to learn how AI can guide