PhD Studentship: High Performance Computing Methodologies for Guiding the Design of New Antibiotics

University of Southampton - Engineering & the Environment

The development of bacterial resistance to antibiotics is a major problem for modern healthcare. A recent World Health Organization report stated: “resistance to common bacteria has reached alarming levels in many parts of the world and that in some settings, few, if any, of the available treatment options remain effective for common infections”. In other words the number of antibiotics that are still effective is quickly diminishing- new antibiotics are therefore urgently needed. 

To design novel, effective and efficient antibiotics, it is imperative that we ask: (i) how do existing antibiotics work? (ii) How can we weaken the membranes that protect bacteria?  

As a step towards addressing these questions, in this project we aim to simulate the process of antibacterial permeation through realistic models of bacterial membranes. Crucially, we shall adopt a multi-scale approach in which fine-grained (quantum level and atomistic-level) simulations will be used to study specific interactions and chemical reaction mechanisms, and a more coarse-grained approach will be used to study larger, morphological behaviour. This project builds on our world-leading expertise in molecular modelling and simulation of bacterial membranes and our experience of large-scale quantum level calculations.  

New methodologies to bridge the quantum, atomistic and coarse-grain levels will be developed as part of the project.  

We are looking for an applicant with a background in chemistry physics, mathematics, or computer science, and an appetite to learn and research across conventional discipline boundaries.  

The stipend is at the standard EPSRC levels. More details on facilities and computing equipment are available 


If you wish to discuss any details of the project informally, please contact Syma Khalid, Email: 

This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling (

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