PhD opportunities

Understanding drug resistance in M. tuberculosis by applying machine learning to a national genomic census

Tuberculosis (TB) kills more people globally than any other infectious disease, and progress towards its elimination is stagnating amid concerns about prevalence of drug resistance. Different lineages of M. tuberculosis differ in both their intrinsic drug susceptibility and their propensity to acquire antimicrobial resistance.

Most studies of resistance use datasets enriched for drug-resistant isolates, creating an ascertainment bias that limits our understanding of how resistance evolves in the wider bacterial population. In this joint project between the UK Health Security Agency (UKHSA) and the University of Birmingham, we will analyse a national collection of M. tuberculosis genomes from England. The collection brings together drug-susceptible and drugresistant isolates with linked enhanced surveillance records and epidemiological data. We will use existing resistance catalogues to characterise drug resistance across lineages and apply genome-wide association methods to investigate genetic factors associated with resistance emergence, pathogenicity and heteroresistance. To address gaps in these catalogues, we will also examine whether advanced machine learning approaches such as genome and protein language models can improve resistance prediction for rare and novel genotypes.

The student will develop skills in microbial genomics, bioinformatics and applied machine learning through investigating how emerging machine learning methods can support effective surveillance of TB and other pathogens.

Supervisory team:

• Professor Nick Loman
• Dr Esther Robinson
• Dr Bede Constantinides

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Plasmids are principal vehicles of antimicrobial resistance dissemination, moving resistance determinants across bacterial lineages, host species, and environmental reservoirs. Which plasmids warrant concern, and in which host contexts, is harder to establish: current tools describe plasmid content and mobility but do not capture host context or the uncertainty associated with incomplete genomic data. This project aims to develop machine learning models that score plasmid risk from surveillance data of varying quality for use in public health decision-making.

Risk scores will be derived from features such as mobility potential, resistance-gene content, and host range, inferred in the first instance from genome assemblies, with training and evaluation temporally separated so that scores are tested against subsequent dissemination rather than prevalence already recorded. Whether read-based sketches can approximate assembly-based performance is a secondary question, and one that determines how far the approach extends to unassembled or lower-quality data.

A key aim will be to develop models that are interpretable as well as predictive by contextualising inference with biological sequence composition, plasmid backbone relatedness, protein-level features of mobility, virulence, and resistance genes, host metadata, and epidemiology. Validation will proceed from characterised clinical isolates, where phenotype is known, to closed plasmid sequences, where structure is certain, to independent metagenomes, where neither is available. Performance, robustness to fragmented input, and generalisation across bacterial lineages will be assessed at each stage.

Supervisory team:

• Professor Willem van Schaik
• Dr Bede Constantinides
• Dr Charlene Rodrigues

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Metagenomic insights into respiratory microbiome dynamics across the disease severity spectrum

Respiratory infections are a major cause of morbidity and mortality, however, important gaps remain in our understanding of their aetiology, early risk stratification, and the role of microbial communities in disease.

This project will apply advanced computational methods to analyse large-scale metagenomic sequencing data from patients across the respiratory disease severity spectrum, ranging from adults presenting with respiratory symptoms in A&E to patients with severe pneumonia in intensive care. The project aims to define how the respiratory microbiome changes with disease severity.

Initial work will focus on microbial profiling and genome reconstruction to characterise bacterial, viral, and fungal communities. Particular attention will be given to the virome, including bacteriophages and anelloviruses, as potential indicators of microbial imbalance and disease progression. The project will investigate interactions between microbes, with a focus on phage–bacteria dynamics, and define ecological states associated with stable and disrupted communities. Machine learning models will then be developed to integrate bacterial, viral, and ecological microbiome features for improved pathogen detection, severity classification, and early identification of patients at risk of deterioration. The project will deliver both fundamental insights into respiratory microbial ecology and practical computational tools with potential clinical application.

The aims of this project include:

• To characterise the respiratory microbiome across a spectrum of disease severity and antibiotics exposure, using metagenomic sequencing data.

• To investigate interactions between bacteria, viruses including bacteriophages, and other microbes, and define ecological states of infection.

• To develop and validate predictive models for pathogen detection, disease severity classification, and risk of clinical deterioration.

• To assess the role of the virome as a complementary source of diagnostic and prognostic information.

This project requires a computational/bioinformatics background and will provide exposure to clinical and public health research. It offers opportunities to collaborate with clinicians and microbiologists, supporting the development of both technical and translational skills.

Supervisory team:

• Professor Nick Loman
• Dr Meera Chand
• Dr Sofia Morfopoulou

Register your interest on FindaPhD

Applications of a two page CV and covering letter including your experience, suitability and motivation should be sent to hpru-phgenomics@contacts.bham.ac.uk by 23rd November 2026.

1. Can international students apply?

No. We are only able to accept students who are eligible to pay Home/UK fees.  Eligibility details can be found on the Gov.uk website   

2. Can clinicians apply?

Yes, as long as they are eligible for home fees. However, the available stipend will be paid at the standard UKRI rate (£21,805) and we are not able to support clinical salary rates.

3. Can I apply for more than one project?

Yes. If there is more than one project that you are interested in you can apply to more than one. You must make this clear in your application.

4. Can I undertake this programme part-time?

Part-time study is possible, but there are restrictions on the total length of the funding so we encourage you to discuss this with the potential supervisor.

5. Are these projects suitable for distance learning?

No. These projects require in person study.