Sofya Titarenko

Fully funded PhD opportunity at the University of Leeds in the areas of statistics , applied statistics , epidemiology , data science , and mathematical modelling . The project aims to enhance the Farrington algorithm for infection prevention and control, with a focus on spatiotemporal modelling and adaptive modelling approaches . This is a strong fit for applicants interested in quantitative research at the intersection of public health and data-driven methods. The post is being shared by Sofya Titarenko , Lecturer at the University of Leeds. The opportunity is specifically for home students . Eligibility highlights include a strong BSc in Mathematics , MMath , or MSc , plus a solid background in mathematical, statistical, or related quantitative methods. The post indicates that the PhD is fully funded . Application deadline: 30 June. Interested candidates should review the project description linked in the post and apply before the deadline.

Fully funded PhD opportunity at the University of Leeds in the UK for a student interested in statistics , medical statistics , mathematics , and infectious disease modelling . The project focuses on developing new statistical methods to detect unusual patterns in healthcare-associated infections . It builds on the Farrington model and aims to introduce modern spatiotemporal and adaptive approaches that can support real Infection Prevention and Control teams. This is a 3.5-year funded PhD and is aimed at UK/home students . The post is especially suitable for applicants with a strong quantitative background and programming experience in R or Python . An interest in real-world health systems and infectious disease applications would be a strong fit. Supervisor/announcer details in the post identify Sofya Titarenko , a Lecturer at the University of Leeds , as the contact person. The announcement includes a LinkedIn profile and a link to the full post with application information. If you are looking for a research-focused PhD with healthcare impact, this opportunity combines statistical methodology, public health relevance, and applied computational work.