Research Fellow Department of Applied Health Sciences 106738- 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 36636 to 46049 with potential progression once in post to 48822

Grade: 7

Full Time Fixed Term contract up to February 2030

Closing date: 5th January 2026

Background

We are seeking a highly motivated researcher in statistics geostatistics data science or a related field to join our research team in the Department of Applied Health Sciences. The successful candidate will contribute to a Gates Foundation funded project focused on developing and applying advanced spatial and spatio-temporal statistical methods to inform disease mapping and control strategies in low-resource settings.

The project offers a unique opportunity to work at the intersection of statistical methodology epidemiology and global health within a vibrant international network coordinated by the newly established Geostatistics fro Population Health research group at the University of Birmingham. The research will involve developing new geostatistical approaches to integrate multi-country disease surveillance data and generate policy-relevant outputs.

The post holder will work under the guidance of Prof. Emanuele Giorgi and Dr Claudio Fronterre contributing to both methodological innovation and applied statistical analyses in the field of disease mapping. The role will involve close collaboration with the NTD Modelling Consortium (University of Oxford) the Task Force for Global Health and other international partners across Africa Asia and the Americas. The post holder will also support the development of open-source software tools and reproducible analytical workflows in R to facilitate large-scale data analysis and dissemination.

This role is particularly suited to candidates with a strong quantitative background who is enthusiastic about translating statistical methodology into impactful applications in global health. We welcome applicants with training in statistics applied mathematics computer science or epidemiology especially those with strong quantitative and coding skills and an interest in spatial data analysis. Prior experience in disease mapping is not essential and training will be provided to develop specialist expertise.

About the project:

The Geostatistics for Global Health (GGH) initiative is a four-year Gates Foundationfunded programme led by Dr Claudio Fronterre and Prof Emanuele Giorgi at the University of Birmingham in partnership with the NTD Modelling Consortium (University of Oxford) the Task Force for Global Health WHO/ESPEN and regional research institutions across Africa Asia and the Americas.

The project aims to develop validate and operationalise advanced spatial and spatio-temporal statistical methods for mapping and analysing neglected tropical diseases (NTDs). These methods will enhance the precision of disease surveillance improve targeting of control interventions and strengthen programme evaluation across endemic regions.

GGH will deliver open-source geostatistical tools reproducible R-based analytical pipelines and accessible training resources to ensure sustained use of these methods by partner institutions and national disease programmes. A key focus is on embedding geostatistical modelling capacity within African research centres to enable independent analysis and local ownership of spatial data systems.

The projects research portfolio includes methodological innovation in geostatistical model development integration of serological and entomological data modelling of infection intensity and the design of surveillance and post-elimination sampling strategies. Close collaboration with operational partners ensures that scientific advances translate directly into programme-relevant decision tools thereby contributing to global NTD elimination goals.

Role Summary

• Work with project investigators (Dr Claudio Fronterre and Prof. Emanuele Giorgi) to develop and apply new spatial and spatio-temporal statistical methods for disease mapping.

• Develop and implement R-based analytical pipelines for model fitting simulation prediction and visualization.

• Contribute to the testing validation and documentation of geostatistical models for operational use.

• Analyse multi-country disease datasets to generate high-resolution prevalence and uncertainty maps.

• Support the preparation of academic publications conference presentations and stakeholder reports.

• Collaborate proactively with international partners participating in regular project meetings and workshops.

• Contribute to training and capacity-building activities with regional partners and early-career researchers.

• Ensure reproducibility and transparency of analyses through version-controlled and well-documented codebases.

Main Duties

The responsibilities may include some but not all of the following:

• Develop document and test novel spatial and spatio-temporal statistical methods.
• Write maintain and optimize R code for model implementation and simulation studies.
• Conduct exploratory analyses model validation and visualization of spatial and temporal trends.
• Apply knowledge in a way which develops new intellectual understanding.
• Disseminate research findings for publication research seminars etc.
• Collaborate with researchers across the GGH network actively participating in meetings and joint research activities.
• Support the writing of scientific papers technical reports and policy briefs for diverse audiences.
• Contribute to developing new models techniques and methods.
• Contribute to the preparation of research proposals and funding bids.
• Supervise or provide guidance to MSc and PhD students involved in related projects.
• Undertake management and administrative tasks related to project coordination and reporting.
• Promote equality diversity and inclusion fostering an inclusive research culture within the team.

Person Specification

• PhD (or near completion) in Statistics Geostatistics Data Science or a related quantitative discipline.
• Strong theoretical understanding and practical experience in spatial or spatio-temporal statistical modelling.
• Advanced proficiency in R programming including data processing simulation and model implementation.
• Demonstrated ability to apply statistical methods to real-world data.
• Excellent analytical and problem-solving skills with attention to reproducibility and detail.
• Effective communication skills including scientific writing.
• Ability to work both independently and collaboratively in a multidisciplinary environment.
• Knowledge of the protected characteristics of the Equality Act 2010 and how to actively ensure in day to day activity in own area that those with protected characteristics are treated equally and fairly.

Informal enquiries to Emanuele Giorgi email:

Use of AI in applications:We want to understand your genuine interest in the role and for the written elements of your application to accurately reflect your own communication style. Applications that rely too heavily on AI tools can appear generic and lack the detail we need to assess your skills and experience. Such applications will unlikely be progressed to interview.

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 everyoneon our website.