Research Fellow (Statistics) - Department of Applied Health Sciences - 106065 - Grade 7

Position Details

Department of Applied Health Sciences, School of Health Sciences, College of Medicine and Health

Location: University of Birmingham, Edgbaston, Birmingham UK

Full time starting salary is normally in the range £36,130 to £45,413 with potential progression once in post to £48,149

Grade: 7

Full Time, Fixed Term contract up to August 2028

Closing date: 17th August 2025

Please note: We are advertising for both a Research Associate (106111) and a Research Fellow (106065), however there is only 1 post available.

Background

We are seeking a highly motivated researcher in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project looking at the design and analysis of randomised trials of interventions to reduce the risk of vector-borne diseases (e.g. malaria and dengue). The Project offers an interdisciplinary opportunity to work in at the intersection of statistics and epidemiology, and features an exciting case study involving novel GM and gene drive interventions for control of malaria-transmitting mosquitoes, with opportunities to build an international research portfolio.

The post holder will work on developing and testing study design and data analysis methods, particularly related to cluster randomised trial designs and working with infectious disease models. You will work within a multidisciplinary team with close links to Imperial College London and the Ifakara Health Institute in Tanzania, including clinicians, epidemiologists, and statisticians. Your work will contribute to a supporting the design of upcoming first field trials of novel technologies in infectious disease control.

This role is particularly suited to candidates with a strong quantitative background. We welcome applicants with training in mathematics, statistics, health economics, computer science, or epidemiology, particularly those with good numeracy and a strong interest in applied health research. We do not require a previous background in the topic area of the post, and training and support will be provided to develop skills in the appropriate area. Familiarity with statistical analysis software (e.g., STATA, R, SPSS) or computer programming (e.g. C++, Python, R) and experience working with health-related data will be advantageous. The ability to work independently, manage multiple tasks, and communicate findings clearly is essential.

About the Project:

Novel GM interventions for mosquito control could represent a step change in progressing towards elimination of the world’s most harmful vector-borne diseases, including malaria and dengue. However, conducting field trials of such interventions is challenging due to complex logistics and uncertain outcomes. We aim to develop novel methodological strategies that can reduce the resource requirements to rigorously evaluate new and uncertain community-level interventions in infectious disease control. A key component relates to harnessing advanced mechanistic disease transmission models to improve prediction of clinical trial outcomes (e.g. doi: 10.1038/s41467-024-53065-z). Given the uncertainty associated with intervention outcomes, we also aim to explore adaptive trial designs to enable studies to start small and refine their design as they progress. We will work closely with co-investigators from the Ifakara Health Institute (Tanzania) to ensure the methods are feasible and implementable in disease-endemic settings, and to improve impact. The project will involve a series of stakeholder engagement workshops, with opportunities for travel to Tanzania.

Role Summary

• Work with the study investigators (Prof. Sam Watson (University of Birmingham), Dr Penelope Hancock (Imperial College London) and Prof. Fredros Okumu (University of Glasgow/Ifakara Health Institute) to develop and test new methods for trial design and analysis. 
• Developing statistical models and code to run the proposed methods.
• Developing and running simulation-based analyses of the new methods.
• Contribute to generating academic publications and reports.
• Contribute to public and stakeholder engagement workshops 

Main Duties

The responsibilities may include some but not all of the responsibilities outlined below.

• Contribute to the development, documentation, and testing of new statistical methods.
• Develop code to run and evaluate statistical models, with the support of project supervisors.
• Support writing of research outputs for academic and lay audiences, and contribute to the development and running of stakeholder engagement activities in African countries.
• Contribute to writing bids for research funding.
• Apply knowledge in a way which develops new intellectual understanding.
• Disseminate research findings for publication, including research seminars and conferences.
• Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline.
• Undertake management/administration arising from research.
• Contribute to developing new models, techniques and methods.
• Contribute to Departmental/School research-related activities and research-related administration.
• Contribute to enterprise, business development and/or public engagement activities of manifest benefit to the College and the University, often under supervision of a project leader.
• Provide guidance, as required, to support staff and any students who may be assisting with the research.
• Deal with problems that may affect the achievement of research objectives and deadlines.
• Promote equality and values diversity acting as a role model and fostering an inclusive working culture.

Person Specification

• PhD (or near to completion or equivalent experience) in a relevant field, including statistics, mathematics, computer science, epidemiology. 
• Strong mathematical and quantitative skills. Experience in the implementation of mathematical or statistical models and model fitting, including Bayesian model fitting, is desirable but not essential.
• Familiarity or experience of management and analysis of large multidimensional real world data sets using Stata, R, Python, or similar. Knowledge of C++ would be advantageous but not essential.
• Ability to communicate complex information clearly.
• Excellent ability to work as a team and independently including co-ordinating own work with others to avoid conflict or duplication of effort.
• Understanding of and ability to contribute to broader management/administration processes.
• Contribute to the planning and organising of the research programme and/or specific research project. 

Further particulars can be found here

Informal enquiries to Samuel Watson, email: S.I.Watson@bham.ac.uk 

We believe there is no such thing as a 'typical' member of University of Birmingham staff and that diversity in its many forms is a strength that underpins the exchange of ideas, innovation and debate at the heart of University life. We are committed to proactively addressing the barriers experienced by some groups in our community and are proud to hold Athena SWAN, Race Equality Charter and Disability Confident accreditations. We have an Equality Diversity and Inclusion Centre that focuses on continuously improving the University as a fair and inclusive place to work where everyone has the opportunity to succeed. We are also committed to sustainability, which is a key part of our strategy. You can find out more about our work to create a fairer university for everyone on our website.