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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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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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enzymes. Mapping bacterial defence systems to infer predictive features of co-evolutionary dynamics. Impact and Outlook This project will: • Advance understanding of microbial co-evolution. • Deliver a
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PhD Studentship: Nanopore Technology for Rapid and Accurate Measurement of Antibiotic Concentrations
conditions and characterise the dynamic range and sensitivity of the nanopore sensors Develop multiplexing strategies for simultaneous detection of multiple antibiotic classes Perform proof-of-principle
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in academia, biotech, and pharmaceutical R&D. Research Environment: The Orengo and Lasso labs provide a dynamic, inclusive, and multidisciplinary research environment at the intersection
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). Design and fabricate patterned surfaces optimised for enzyme immobilisation. Assess synergistic antibiofilm efficacy under static and dynamic (flow-based) biofilm models. Apply advanced microscopy, protein
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(SONATA, EP/V028626/1) and brings together expertise in microfluidics, fluid dynamics, nanoparticle engineering, and dental microbiology. Approach and Methods: Engineer in vitro models of bacterial biofilm
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, with collaboration across synthetic biology, computational biology, and microbiology. The student will work within a dynamic, interdisciplinary team with access to state-of-the-art facilities and