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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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: Machine Learning Molecular Dynamics. The project involves the development and application of machine learning methods that enable a major boost of the time and length scales accessible to ab-initio/first
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with experimental virologists to validate computational predictions Impact and Outlook: This project will uncover the untapped structural and functional potential of bovine UL-CDRs, laying the groundwork
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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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Supervisors: Dr. Michele Crotti Dr. Martyna Michalska Abstract: Biofilm-associated infections account for nearly 80% of human microbial infections and are notoriously resistant to antibiotics and
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such as, but not limited to, chemical, pharmaceutical, biochemical, or mechanical engineering; pharmaceutical sciences; materials science; or related areas. Applicants from computer science with relevant
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for Doctoral Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.
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Training in Engineering Solutions for Antimicrobial Resistance. Further details about the CDT and programme can be found at AMR CDT website Applications should be submitted by 12th January 2026.
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for microbiology and screening assays, MRC and Wellcome-funded. Both labs promote an open, collaborative culture and have a strong track record of PhD supervision. Desirable Prior Experience Some prior experience in
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CDT and programme can be found at AMR CDT webiste Applications should be submitted by 12th January 2026.