Antimicrobial resistance (AMR) poses a threat to human health and modern medicine. Antibiotic use drives the development of resistance. Understanding how to safely reduce use is crucial in tackling AMR. When treating bacterial infections, antibiotics are often started before knowing which antibiotics would be effective. Theoretically, ignoring AMR, clinicians would always prescribe broad-spectrum antibiotics to ensure effectiveness. However, in reality, antibiotic selection needs to be balanced against the risk of increased AMR. Additionally, rates of resistant infections vary between patients. This interdisciplinary research will develop and evaluate statistical prediction and machine learning models to improve prediction of antibiotic effectiveness for individual patients. This personalised medicine approach could lead to increasing the chances of choosing effective antibiotics, while also minimizing the risk of AMR.
This project will involve:
• Development of novel statistical/machine learning models for predicting antibiotic resistance
• Evaluation of the clinical impact of infections
• Evaluation of the health-economic impact of introducing a decision-support tool (based on the prediction models) into the NHS
This PhD offers a valuable opportunity to: work in both academic and policy environments; learn a unique skill-set in an area of shortage (yet high demand); and use developed models to inform policy in a high priority area.
Potential applicants wishing for further information are encouraged to contact Dr David Eyre at [email protected]
Benefits of joining the Medical Research Foundation National PhD Training Programme in AMR Research:
• All PhD projects will be based within interdisciplinary research consortia funded by the UKRI Cross-Council AMR Initiative.
• All students will have access to enhanced training opportunities including residential skills and training courses, cohort-building activities, and annual conferences. All are designed to expose students to a range of discipline-specific languages and interdisciplinary research skills, which are essential for enabling them to thrive as multidisciplinary AMR researchers.
• PhD students will undertake a fully-funded 3-month interdisciplinary AMR project allowing them to work outside of their primary research area or an elective placement in industry, publishing, media, policy development or in AMR-relevant charities and organisations.
• All Medical Research Foundation-funded PhD students will also be part of a wider cohort of 150 PhD students from across the UK who are also studying AMR. The cohort will have access to a bespoke, innovative online learning environment, which will facilitate peer-to-peer networking, question setting and mentoring.
Further information can be found on our website: https://www.bristol.ac.uk/cellmolmed/study/postgraduate/amr/
Applicants must have obtained, or be about to obtain, at least a 2.1 honours degree in a relevant subject. Applicants must ensure that they meet the eligibility requirements of the University of Oxford.
To qualify for Home tuition fee status, you must be a UK or EU citizen who has been resident for 3 years prior to commencement. Please note that overseas students not eligible for Home (UK/EU/EEA citizens) tuition fee status will be eligible for funding through this Programme but the student must pay the difference between the annual Home tuition fee and the tuition fees required for overseas students. Overseas students should be able to demonstrate adequate financial support to cover the difference between the Home/EU fee and the overseas fee. Applicants are also required to meet the University of Oxford’s English language requirements
University application guide: https://www.ox.ac.uk/admissions/graduate/applying-to-oxford/application-guide?wssl=1
Online applications portal: http://www.graduate.ox.ac.uk/applyonline
Please select DPhil in Clinical Medicine as your programme of study and write: ‘Using Machine Learning and Statistical Prediction Models to Improve Empirical Antibiotic Prescribing’ under ‘proposed research and title of research project’ in your application form. Please indicate Dr David Eyre as proposed supervisor. Proposed funding source should be: Medical Research Foundation National PhD Training Programme in AMR Research. Departmental studentship reference code: 1024. Under Statement of study plans/Research Proposal, please state that you are applying for the Medical Research Foundation National PhD Training Programme in AMR Research studentship.