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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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annually, with life-threatening consequences for immunocompromised individuals. With few antifungal drugs available and resistance on the rise, this project explores a novel strategy: engineering the fungal
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PhD Studentship: Nanopore Technology for Rapid and Accurate Measurement of Antibiotic Concentrations
samples. Nanopore technology, which detects molecules via changes in ionic current as they pass through nanometer-scale pores, has revolutionised nucleic acid sequencing and holds untapped potential
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rather than the structured biofilms found in real-world environments. This project investigates how engineered surface topographies influence HGT dynamics, aiming to develop design principles for materials
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Engineering, Physical Sciences, and Mathematical Sciences. Why chose UCL? UCL has a history of academic excellence and is consistently ranked among the world's top universities, with many of our faculties
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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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-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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, translational insight, and valuable opportunities for career mentoring and networking with industrial and clinical experts. Desirable Prior Experience: Background in an aligned engineering or science discipline
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Supervisors: Prof Manish Tiwari Prof Shervanthi Homer-Vanniasinkam Clinical Partner: The Royal National Orthopaedic Hospital (RNOH) Collaborator: Dr. Priya Mandal – UCL Mechanical Engineering
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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