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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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PhD Studentship: Genetic modifiers to untangle disease mechanisms of RFC1 repeat expansion University College London � Queen Square Institute of Neurology Project: Biallelic repeat expansions in
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will be considered on a case-by-case basis. The scholarship is funded by G-Research. Research focus The PhD topic must focus on Artificial Intelligence (AI) in relation to the research areas of one
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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.