PhD Studentship – Next Generation Farrington Model for Infection Prevention and Control
University of Leeds
Location: Leeds, United Kingdom
General Description:
The University of Leeds is offering a fully funded PhD studentship focused on advancing statistical methodologies for infection prevention and control through the development of a next-generation Farrington model. This project aims to enhance the traditional Farrington algorithm by incorporating spatiotemporal modelling and adaptive baseline capabilities to improve outbreak detection and surveillance systems.
The research will involve developing novel statistical and computational approaches to detect infectious disease outbreaks more accurately and efficiently. The successful candidate will work with large-scale epidemiological datasets and contribute to improving real-time public health monitoring tools. The project sits at the intersection of statistics, data science, and epidemiology, with potential applications in healthcare systems and public health agencies.
The student will be based within a vibrant research environment at the University of Leeds, benefiting from supervision by experts in statistical modelling and health data science. The role includes opportunities for interdisciplinary collaboration, professional development, and dissemination of findings through academic publications and conferences.
This is a full-time PhD position with funding support that typically includes tuition fees and a maintenance stipend, in line with UKRI rates.
Eligibility Criteria:
- A first-class or strong upper second-class undergraduate degree (or equivalent) in statistics, mathematics, data science, computer science, or a closely related discipline
- A relevant master’s degree is desirable but not essential
Required Expertise/Skills:
- Strong background in statistical modelling and data analysis
- Proficiency in programming languages such as R or Python
- Interest in epidemiology, public health, or infectious disease modelling
- Ability to work with large and complex datasets
- Strong analytical and problem-solving skills
- Excellent written and verbal communication skills
- Ability to work independently and as part of a collaborative research team
Salary Details:
Fully funded studentship covering tuition fees and a tax-free maintenance stipend in line with UKRI rates
Application Deadline:
Not specified