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nanoparticles can protect them and help deliver them precisely where they are needed. However, designing suitable AMP-nanoparticle combinations is complicated. Many formulation ingredients, concentration ratios
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Develop an active learning-driven platform for compound selection and optimisation Integrate robotic sample preparation, automated data acquisition, and computational analysis Advance five existing
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diagnostics, empirical antibiotic use is common, exacerbating resistance. This project aims to develop a next-generation lateral flow assay (LFA) platform for rapid, ultrasensitive detection of RTI pathogens
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. Synthetic analogues will be developed and screened alone and in combination with existing antimicrobials. The ultimate goal is to design novel chemotherapeutic combinations that disrupt cell wall remodelling
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-penetrating capabilities Evaluate delivery efficiency in cell-based models mimicking lung and immune tissues Identify structure–function relationships to inform rational design of future mucosal delivery
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Supervisors: Dr. Duygu Dikicioglu Prof. Eli Keshavarz-Moore Abstract: Antifungal resistance is a growing and under-recognised global health threat. Fungal infections affect over a billion people
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the synergistic effects of monoclonal antibodies and colistin. Building on extensive preliminary data, the project aims to uncover the mechanisms behind this synergy using advanced biophysical and molecular
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Supervisors: Dr Darren N. Nesbeth Prof Eli Keshavarz-Moore Abstract: Horizontal gene transfer via conjugative plasmids is a major but underexploited driver of antimicrobial resistance (AMR) spread
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. Atomistic Simulation of electronically excited processes in molecules and materials is essential for our understanding of the working principles of emerging energy conversion technologies, e.g. solar cells
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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