EastBio: When a negative test lies: modelling imperfect detection in infection and antimicrobial resistance.

Details

This fully funded, 4-year PhD project is part of a competition funded by the BBSRC EASTBIO Doctoral Training Partnership.

Assume a bacterium is sampled from a patient and the test returns negative. It is repeated, and each time returns negative. Repeated negatives increase confidence that no infection is present. But when detection is imperfect, a negative can mean the organism was there and missed, not that it was absent. These are false negatives, and they are most likely when the very thing that reduces an infection also makes it harder to detect. Antibiotics are the clear case: they reduce bacterial burden, which is what a culture needs to find the organism, so a treated patient can test negative while still carrying it. If misread as cure, the decision to stop treatment rests on a test that was never going to find anything. The same blind spot runs through antimicrobial resistance surveillance, where failing to detect resistant organisms or resistance genes distorts estimates of prevalence and transmission.

Ecologists ran into this problem decades ago. They could not tell whether a species was truly absent or simply undetected, so they built hierarchical models that treat the true state and the act of observing it as two separate things. This project takes that idea into human infection. Cystic fibrosis provides an ideal setting because patients undergo repeated microbiological sampling over many years, experience recurrent antibiotic exposure, develop and select multidrug-resistant organisms and show fluctuating levels of apparent colonisation. You will build Bayesian models that estimate who is really carrying the organism, how it comes and goes over time, and how much apparent clearance is just detection failing. Running a range of laboratory assays on the same clinical samples gives the models enough leverage to pull the biology apart from the observation, and to show how accounting for imperfect detection changes estimates of pathogen persistence, transmission and resistance, and what that means for reading surveillance and diagnostic data.

The training will cover Bayesian hierarchical modelling, statistical simulation and causal reasoning, alongside bacteriology, antimicrobial susceptibility testing and bacterial phylogenetics, with all quantitative work done in R and Bayesian programming languages. You will sit between statistics and microbiology, supervised by Roos (statistical modelling and observation processes), Hijazi (antimicrobial resistance and bacteriology) and Okoliegbe (NHS diagnostic microbiology).

It would suit a quantitative student who wants to use modelling to understand antimicrobial resistance. No laboratory or medical background is needed, just a willingness to work across statistics and biology.

This project is available for part-time study (Home/UK students only).

Interested applicants should contact the lead supervisor to discuss the project prior to applying. You can do this by selecting the first listed name at the top of this advert and sending your enquiry. Alternatively, you can search for the supervisor through the University of Aberdeen staff directory and email them directly.

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ELIGIBILITY:

Applicants should hold a minimum of a 2:1 UK Honours degree (or international equivalent) in a relevant subject. Those with a 2:2 UK Honours degree (or international equivalent) may be considered, provided they have (or are expected to achieve) a Distinction or Commendation at master’s level.

We encourage applications from all backgrounds and communities, and are committed to having a diverse, inclusive team.

All students must meet the eligibility criteria as outlined in the UKRI guidance on funding for postgraduate training and development. This guidance should be read in conjunction with the Terms and conditions for training funding – UKRI.

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APPLICATION PROCEDURE

For full details and to download the application form, visit: https://biology.ed.ac.uk/eastbio/how-to-apply

Please note that you may only apply for one EASTBIO project.

1. Application submission

Submit your complete application by 23:59 GMT on 16th November 2026 to smmsn-pgrenquiries@abdn.ac.uk.

Send your application as a single email with the subject line: “EASTBIO Application – [Your Name]”

2. Required documents

Your application email must include the following:

• Completed EASTBIO application form
• Academic certificates and transcripts (University education only)
• Two references (one reference may be non-academic)

3. References

All references must be provided using the official EASTBIO reference form (available here).

References can be either:

• Included with your application, or
• Sent directly by referees to smmsn-pgrenquiries@abdn.ac.uk with the subject line: “EASTBIO Reference – [Your Name]”

All references must be received by 23:59 GMT on 16th November 2026.

It is the applicant’s responsibility to ensure both references are submitted by the deadline, as we cannot request them on your behalf.

Important notes

• You may only apply for one EastBio Project.
• No research proposal is required.
• Do not include any additional documents (for example, school certificates, conference papers, or dissertations).
• Incomplete applications or those including superfluous unnecessary documents will not be considered.
• Due to workload constraints, we are unable to follow up on missing documents or process incomplete applications.
• All candidates will be notified of the outcome of their application.
• We will confirm receipt of your applications upon processing. Due to the high volume of applications, this may not be until after the deadline. We ask that you please refrain from contacting us regarding updates or to see if your application has been received.
• For any questions or assistance with the application process, please contact smmsn-pgrenquiries@abdn.ac.uk.

Funding Notes

This fully funded, four-year PhD project is offered as part of a competition within the EASTBIO BBSRC Doctoral Training Partnership. The opportunity is open to both UK and international applicants, although the proportion of international students recruited through the EASTBIO DTP is capped at 30% by UKRI BBSRC.

EASTBIO studentships provide a tax-free UKRI doctoral stipend (estimated at £22,437 for the 2027/28 academic year), a training grant of £5,000 per year for the first three years (and £1,500 in the fourth year), and a travel and conference allowance of £230 per year.

Please note that EASTBIO is unable to fund visa costs or associated healthcare surcharges for international students.