Predicting Disease State Transitions Over Time in Patients with Multimorbidity: A Dynamic Longitudinal Approach with Spatial Effects

About the Project

Are you interested in developing cutting-edge statistical models to address one of healthcare’s biggest challenges—multimorbidity, the coexistence of multiple chronic conditions? This project offers the opportunity to work with real-world UK data and create innovative Bayesian models to predict how chronic conditions progress over time and vary across regions. By combining longitudinal clinical biomarkers, multistate disease transitions, and spatial analysis, you will help develop tools that provide personalised, dynamic risk predictions, supporting early intervention and informing healthcare planning.

Throughout the PhD, you will receive expert training in joint and multistate modelling, Bayesian methods, spatial statistics, and advanced R programming. You will gain hands-on experience with large-scale electronic health records, translating statistical methodology into applied insights and communicating results to interdisciplinary audiences. You will work closely with a supervisory team experienced in joint modelling, dynamic prediction, and multimorbidity research, collaborating with researchers at Newcastle University and the University of Manchester. Opportunities to present your findings at seminars, workshops, and conferences, and to contribute to open-source R tools, will enhance your professional development and scientific impact.

This project uniquely combines methodological innovation with real-world relevance. By examining multimorbidity progression and regional variation, your research will improve understanding of ageing and chronic disease while producing actionable insights for healthcare delivery. You will join a collaborative and supportive environment within the Biostatistics Research Group at Newcastle University, working in partnership with the statistical group at the University of Manchester, benefiting from mentoring, career development, and access to a strong network of statisticians and clinicians.

If you are motivated by solving complex health challenges using modern statistical approaches and want a PhD that balances technical training, applied research, and professional growth, this studentship is an excellent opportunity. By the end of the programme, you will have advanced expertise in Bayesian modelling and dynamic prediction, practical experience with complex healthcare data, and a strong publication profile—preparing you for a career in academia, data science, or quantitative health research.

Funding

Students who have, or are expecting to attain, at least an upper second-class honours degree (or equivalent) in a relevant subject, are invited to apply. Funding is available for Home (UK) students to cover tuition fees, a tax-free stipend at the UKRI rate (indicative amount in year 1 in 2026-27, £21,805) and research costs, for four years. Applicants normally required to cover International fees will have to cover the difference between the Home and the International tuition fee rates. There is no additional funding available to cover NHS Immigration Health Surcharge (IHS) costs, visa costs, flights etc.

Funding for this studentship is awarded on a competitive basis and is not guaranteed; availability will depend on the outcome of the selection process and subject to final approval by the University.

HOW TO APPLY

Please complete the following application form – Google Form

Applicants can only apply for 1 project; any additional applications will not be accepted.

Applicants should send the following documents to FMSstudentships@newcastle.ac.uk:

• a CV (including contact details of at least two academic (or other relevant) referees).
• a Cover letter – stating your project choice, as well as including additional information you feel is pertinent to your application.
• copies of your relevant undergraduate degree transcripts and certificates.
• a copy of your IELTS or TOEFL English language certificate (where required)
• a copy of your passport (photo page).

A GUIDE TO THE FORMAT REQUIRED FOR THE APPLICATION DOCUMENTS IS AVAILABLE

Please submit your documents in the following format only:

• each document should be submitted *as a separate attachment* and should be named as follows: candidate surname, candidate name – document type. For example: Jones, Jamie – CV; Jones, Jamie – cover letter.
• Please submit .pdf documents where possible for your CV, cover letter, transcripts and certificates. Do not submit photos of certificates.
• Do not combine documents into one pdf. You may zip separate documents into a zip file to send via email if required.
• When emailing your application, please use the email subject header: FMS PhD Application 2026

Applications not meeting these criteria may be rejected.

Informal enquiries may be made to the lead supervisor of the project you are interested in.

The deadline for all applications is 12 noon BST (UK time) on Wednesday 20th May 2026.