Sort by
Refine Your Search
-
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
-
. Approach and Methods: Apply deep learning-based modelling and clustering to analyse a curated dataset of hundreds of thousands of UL-CDR sequences Characterise sequence–structure relationships and structural
-
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
-
(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
-
-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
-
-pharmacological antifungal therapies. Approach and Methods: Develop and optimise laboratory models of fungal growth and resistance. Investigate how environmental stress factors (e.g. osmotic and nutrient stress
-
). 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
-
. 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
-
an academic that has supervised previous work, projects or similar), A short research proposal using this template: https://www.overleaf.com/read/bffndqvvkzcv#a53b9d (this template can be copied or downloaded