Research Associate in Statistical Methodology/Biostatistics

We are seeking a motivated and collaborative individual to join our team as a Postdoctoral Research Associate in Statistical Methodology / Biostatistics. This role offers an exciting opportunity to contribute to the NIHR-funded project “Joint Modeling for Dynamic Risk Prediction in Extreme Clinical Cases Using Longitudinal Patient Data” within the Department of Mathematics, School of Natural Sciences. You will work in a dynamic and inclusive environment with interdisciplinary clinical and methodological research teams across the University and NIHR network.

You will:

• work with Dr. Taban Baghfalaki on Joint Modeling for Dynamic Risk Prediction in Extreme Clinical Cases Using Longitudinal Patient Data;
• develop advanced statistical methodology for dynamic risk prediction using longitudinal patient data;
• collaborate with interdisciplinary clinical and methodological research teams across the University and NIHR network;
• develop reproducible statistical software, including R package development.

We welcome candidates who bring diverse perspectives, experiences, and approaches to research and collaboration.

About You

We encourage applications from individuals with a wide range of backgrounds and experiences. You should demonstrate:

Essential Criteria

• a PhD or equivalent research experience in Statistics, Biostatistics, Data Science, or a closely related discipline, or expect shortly to obtain one;
• research experience in an area of Statistics/Biostatistics of relevance to the project;
• a willingness and ability to learn rapidly any technical background knowledge required for the project which you do not already possess;
• strong communication skills in written and spoken English;
• a personal commitment to equality, diversity, inclusion and accessibility, and a willingness to work with colleagues from diverse backgrounds.

Desirable Criteria

• research experience in longitudinal data analysis, survival analysis, or joint modelling;
• experience in extreme value methods, quantile joint modelling, Bayesian methodology, and the analysis of large-scale (big data) datasets would be advantageous;
• proficiency in developing reproducible statistical software, including R package development.

We value transferable skills and real-world experience as much as formal qualifications.

Our benefits include:
• Generous employer contribution pension
• 29 days annual leave plus bank holidays, along with Christmas closure
• Ride to work and EV car scheme available

For more information, please see University of Manchester Benefits. You can also find information on our Flexible and Hybrid working here.

We are an open place of enquiry and challenge. We embrace and celebrate difference, diversity and debate, and we pride ourselves on being a place of education, learning and community where we are able, within the law, to question and test received wisdom, express new ideas and explore controversial or unpopular topics and opinions. Find out more from our Freedom of Speech Policy.

Enquiries about the role, shortlisting and interviews

Name: Dr. Taban Baghfalaki

Email Address: taban.baghfalaki@manchester.ac.uk

General enquiries and administrative support

recruitmentservices.people@manchester.ac.uk

Technical and job portal support

https://jobseekersupport.jobtrain.co.uk/support/home

Applications close at midnight on 15 July 2026.

Further particulars (with person specification) linked below.