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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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engineering, and nanofabrication techniques. Test materials against clinically relevant pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Impact and Outlook: This project will deliver a new
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language model (LLM)-based genome design tools with bioprocess engineering to create next-generation therapeutic conjugative plasmids. These engineered plasmids will be optimised for industrial-scale
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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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healthcare settings. Approach and Methods: Synthesize gold nanostars with optimised optical and surface properties for enhanced plasmonic signal amplification Engineer LFA architectures incorporating salt
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, and biotechnology. Research Environment: The project is a collaboration between UCL Chemistry, Pharmacy, and Chemical Engineering departments. Students will work across multiple labs, benefiting from a
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basic microbiology, immunology, and/or microscopy would be an advantage, but not essential. How to apply This project is offered as part of the Centre for Doctoral Training in Engineering Solutions
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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.