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Deadline: 06 January 2027
South West, All England
This project is one of a number that are in competition for funding from the University of Bath for entry in October 2027.
Overview of the Research:
Antimicrobial resistance (AMR) is a serious global threat. Bacteria are constantly evolving ways to become resistant to antibiotics. We need to understand the details of these resistance mechanisms so that we can predict future instances of AMR and develop new antibiotics. One of the key strategies employed by bacteria to develop resistance to a particular drug is to change the protein binding site targeted by that drug. In this way, the drug may bind less effectively, or not at all. These protein mutations originate from mutations within the bacteria’s DNA. The rise in genome sequencing has led to the existence of large-scale datasets of experimentally known resistant mutations. Molecular dynamics is a powerful technique for understanding and predicting how these resistant mutations lead to resistance. By modelling individual atoms, or groups of atoms, as ‘beads’ and simulating how these beads interact over time using classical mechanics, we can build up a picture of how the protein and drug interact over time and thereby gain insights into the key chemical, physical and dynamical processes underlying resistance. Additionally, machine learning is a powerful and rapid method to identify and predict trends from large datasets. In this project you will combine molecular dynamics simulations with machine learning to understand and predict instances of AMR in, for example, Mycobacterium tuberculosis or Enterobacterales. You will have the opportunity to develop your coding skills, utilise high performance computing facilities, and test additional computational methods as the project evolves in line with your own interests. The overall aim is to investigate the molecular mechanisms underlying AMR in bacteria. The long-term impact of your research will be to inform drug design and prevent the rise in AMR, thereby improving quality of life and reducing AMR-associated deaths.
References
1. Global burden of bacterial antimicrobial resistance 1990–2021: a systematic analysis with forecasts to 2050. Naghavi M, Vollset SE, Ikuta KS et al., The Lancet (2024), 404(10459), 1199-1226. https://doi.org/10.1016/S0140-6736(24)01867-1 https://creativecommons.org/licenses/by/4.0/
2. Predicting rifampicin resistance in Mycobacterium tuberculosis using machine learning informed by protein structural and chemical features. Lynch CI, Adlard D, Fowler PW, ERJ Open Res (2025), 11(3), 00952-2024. https://doi.org/10.1183/23120541.00952-2024 https://creativecommons.org/licenses/by/4.0/
The successful candidate will have, or be about to obtain, a high Upper Second Class or First Class UK Honours degree (or the international equivalent) in a relevant subject (for example, chemistry, biochemistry, biology, physics, materials science or computer science). A master’s level qualification would also be advantageous. Previous computational and coding experience, and familiarity with the Linux command line, is desirable but not essential.
Non-UK applicants must meet the programme’s English language requirement by the application deadline.
Candidates may be considered for a University of Bath studentship tenable for 3.5 years. Funding covers tuition fees, a stipend (£21,805 per annum in 2026/7) and access to a training support budget.
Studentships are open to both Home and International students; however, International applicants should note that funding does NOT cover the cost of moving to the UK, obtaining a student visa (https://www.gov.uk/visa-fees/) or payment of the UK healthcare surcharge (https://www.gov.uk/healthcare-immigration-application).
Informal enquiries are encouraged and should be directed to Dr Charlotte Lynch, [email protected]
Formal applications should be submitted via the University of Bath’s online application form for a PhD in Chemistry prior to the closing date of this advert.
IMPORTANT:
When completing the application form:
1. In the Funding your studies section, select ‘University of Bath URSA’ as the studentship for which you are applying.
2. In the Your PhD project section, quote the project title of this project and the name of the lead supervisor in the appropriate boxes.
Failure to complete these two steps will cause delays in processing your application and may cause you to miss the deadline.
More information about applying for a PhD at Bath may be found on our website.
PLEASE BE AWARE: Applications for this project may close earlier than the advertised deadline if a suitable candidate is found. We therefore recommend that you contact the lead supervisor prior to applying and submit your formal application as early as possible.
Equality, Diversity and Inclusion:
We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.
If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.
Home and Overseas applicants
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