About the Project
Fungal infections have become a major and growing global health crisis, causing an estimated 3.8 million deaths each year—surpassing the toll of diseases like tuberculosis and malaria. These infections disproportionately affect vulnerable populations, such as people with weakened immune systems, including those with HIV/AIDS, cystic fibrosis, cancer patients, and organ transplant recipients. The problem is compounded by the rise of drug-resistant fungal strains and the lack of effective…
diagnostics and treatments, especially in low- and middle- income countries. Climate change and global travel are also contributing to the emergence and spread of new, more resilient fungal pathogens. Addressing this neglected threat requires urgent investment in research, improved diagnostics, wider access to antifungal medicines, and global collaboration to reduce the burden of fungal diseases.
The antimicrobial management of patients with bloodstream infections is considered time critical, and in an era of increasing antimicrobial resistance, the accurate and rapid identification of the agent of infection and its susceptibility to antifungal drugs is essential to successful clinical resolution. However, susceptibility test results, either by microbroth dilution or by E-test, can take up to 3 days to turn around from microbiological culture. Recent research has demonstrated direct gene sequencing approaches to clinical diagnostics based on marker gene mutations involved in specific resistance mechanisms, but fungal strain heterogeneity currently limits the sensitivity of these relatively costly approaches.
Proteomic approaches with mass spectrometry, and particularly MALDI-ToF mass spectrometry already in routine use in clinical laboratories, potentially offers an inexpensive route to predicting resistance status based on ribosomal and metabolic protein signatures. In the last few years there has been significant progress in machine learning approaches for mass spectrometry based antibiotic susceptibility testing of bacteria, but relatively little for clinical mycology. The goal of this studentship is to harness our expertise in data science and artificial intelligence approaches for interpreting mass spectrometry data (https://bit.ly/3jM69yS) to develop in-silico susceptibility predictors for clinically-important pathogens. To achieve this, the student will be embedded within the UKHSA National Mycology Reference Laboratory with access to their processes, procedures and comprehensive archive of samples, genetic and MALDI-ToF data for patient care and surveillance. From this exemplar classification studies will be selected and then the student will initially apply our current spectral deconvolution methodology to score new samples based on a library of historic acquisitions linked to antimicrobial susceptibility test results. The student will then compare these results to novel artificial intelligence methodology based on representative learning approaches that in a data-driven way jointly learn to differentiate diverse phylogenetic signal from consistent signal from resistance mechanisms. The student will also explore how sample preparation and acquisition parameters can be modified to optimise the model, in a data-driven way. In addition, there will be an opportunity to visit LMIC collaborators at the University of Cape Town to adapt and tune models to key local applications such as azole-resistant Aspergillus fumigatus in cystic fibrosis patients.
The project is suitable for a student who comes from a variety of academic backgrounds, and the supervisory team is experienced in interdisciplinary working. The project will be tailored to the student: we will also consider those with a mathematical/computational background open to learning skills in bioinformatics and laboratory work, or those with a biological/biomedical background who desire skills in basic programming, data science and machine learning.
During the “prep” period, the student would have the opportunity to explore relevant aspects of the disciplines that they are unfamiliar, and this is likely to require significant reading/training in antimicrobial resistance, clinical mycology, and the diagnostic landscape.
Opportunities to shadow the clinical microbiology teams at the UKHSA National Mycology Reference Laboratory will be provided. Through this learning and performing a basic hands-on analysis in each of the strands during the preparatory period, the student will be supported to determine the weight they wish to give to each strand during the PhD, and potentially also the target organisms and resistance mechanisms, where their reading and interaction with clinicians suggest the most clinically valuable avenues. For example, the PhD could either have a larger focus on (i) artificial intelligence methodology for modelling strain and resistance specific signal; or (ii) optimisation of sample preparation and mass spectrometry acquisition to maximise distinguishing signal, or (iii) evaluation of how the approaches can be translated within LMIC scenarios.
How to Apply
A list of all the projects and how to apply is available on the GW4 BioMed website at gw4biomed.ac.uk. You may select up to 2 projects and submit one application per candidate only.
Please complete an application to the GW4 BioMed3 for an ‘offer of funding’. If successful, you will also need to make an application for an 'offer to study' to your chosen institution later.
Please complete the online application form linked from our website by 5.00pm on Wednesday, 21st October 2026. Please note that we may close the application process before the stated deadline if an unprecedented number of applications are received– check the GW4 BioMed website for details and updates. If you are shortlisted for interview, you will be notified from Tuesday, 22nd December 2026. Interviews will be held virtually on 26th and 27th January 2027. Studentships will start on 1st October 2027.
Further Information
For informal enquiries, please contact
GW4BioMed@cardiff.ac.uk
For project related queries, please contact the respective supervisors listed on the project descriptions on the GW4 BioMed website.
Funding Notes
These studentships are funded through GW4 BioMed3 MRC Doctoral Landscape Programme and consist of UK tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum (£21, 805 p.a. for 2026/27, updated each year).
Additional research training and support funding of up to £5,000 per annum is also available.
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