Research Associate/Fellow in Statistics (Fixed-term)

About the role

We are looking for a highly motivated researcher, whose expertise lies in statistics and computational methods, to work with Professors Theodore Kypraios and Philip O'Neill on an NIHR-funded project entitled " Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models ". The overarching aim of the project is to develop the next generation of statistical tools for fitting stochastic epidemic models to infectious disease outbreak data. The post will contribute to cutting-edge methodological research on Bayesian inference for complex stochastic transmission models, as part of a team developing analytic likelihood approximations and neural posterior estimation methods for epidemic data analysis. This role offers an excellent opportunity to work at the interface of statistics and epidemiology, using modern computational approaches to improve inference for real-world transmission models. The project seeks to deliver robust and scalable alternatives to traditional data-augmentation methods, with outputs including open-source software, careful evaluation on synthetic and real outbreak data, and training materials to support wider uptake. The successful candidate will disseminate their work through high-quality publications and contribute to advancing statistical methodology for stochastic epidemic modelling. We believe that talented and inclusive teams deliver the highest quality research and are seeking applications from high quality candidates who enhance the diversity of our existing team. The School is committed to creating opportunities for people traditionally under-represented in Mathematical Sciences and strives to maintain an environment where people can be their authentic selves. You will be able to carry out duties to the highest standard and to evidence how through your experience you will: Undertake original research of international excellence. Develop research objectives and proposals for own and/or collaborative research area. Prepare papers for publication in leading journals and/or contribute to the dissemination at national/international conferences, workshops and meetings resulting in successful research outputs. Identify opportunities and assist in writing bids for research grant applications. Prepare proposals and applications to both external and/or internal bodies for funding, contractual or accreditation purposes. We are looking for a confident, organised researcher who can evidence: A PhD, or equivalent in statistics, machine learning or a closely related discipline, OR near to completion of a PhD. Expert knowledge of statistical inference methods. Excellent communication and organisational skills. The ability to work independently and as part of a multidisciplinary and multicultural team. Networking, actively engaging with and valuing other areas and diverse groups. This full-time post is available from 01 September 2026 [or as soon as possible thereafter] and will be offered on a fixed-term contract until 30/04/2029. Many of our team have flexible working patterns so we're open to discuss flexible working arrangements with you. If it sounds like you would be a good fit, we look forward to hearing from you. For information about the School of Mathematical Sciences and active research themes see: http://www.nottingham.ac.uk/mathematics/index.aspx . To find out more about the People and Culture in the School of Mathematical Sciences please visit our website: https://www.nottingham.ac.uk/mathematics/edi/edi.aspx Informal enquiries may be addressed to Professor Theodore Kypraios email: theodore.kypraios@nottingham.ac.uk . Please note that applications sent directly to this email address will not be accepted. Further details: To help you succeed, we published Candidate Guidance to provide support on the application and interview process. Discover our benefits, visit Your Benefits website. £32,080 to £47,389 per annum, depending on skills and experience (minimum £35608 with relevant PhD). Salary progression beyond this scale is subject to performance.

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