[School of Engineering PhD Scholarships] Multi-Omics Digital Twins for Modelling Brain Ageing and Intervention Response

About the project

About the Project Why do some people maintain good brain health as they age, while others experience more rapid decline? Current “brain-age” models often reduce this complex process to a single score. This PhD project will go further by developing multi-omics digital twins: computational representations that capture an individual’s biological state, model how it may change over time and explore potential responses to intervention. The student will integrate complementary molecular data to study healthy and accelerated brain ageing. Where appropriate, these data may be linked with imaging, cognitive or clinical information. The research will investigate how biological processes interact across different levels and how they contribute to individual differences in ageing trajectories. Advanced computational methods will be used to learn biological representations, model temporal change and estimate intervention response. The project will address important real-world challenges, including incomplete measurements, differences between studies and limited longitudinal data. Models will be tested using independent cohorts and evaluated for predictive performance, reliability and consistency with established biological knowledge. A key innovation is the move from describing brain ageing to modelling it as a dynamic process. The resulting digital twins will not be immediate clinical decision tools; instead, they will provide a rigorous framework for identifying markers of brain resilience, generating testable hypotheses and prioritising potential interventions for further investigation. The project offers interdisciplinary training in multi-omics analysis, computational modelling, machine learning, causal reasoning and responsible health-data research. The student will join Manchester’s wider research communities in ageing, digital health and data science, contributing to the University’s ambition to combine fundamental discovery with research that supports healthier lives. This project is expected to start in September 2027. Before you apply: We strongly recommend that you contact the supervisors for this project before you apply. How to apply: To be considered for this project you must complete a formal application through our online application portal. If you already have an applicant account this link will directly open an application for PhD School of Engineering Scholarships . If you don’t already have an applicant account, please follow the instructions here. . When applying, please specify the full title and supervisor/s of the project, details of your previous study, and names and contact details of two referees. You must also upload a Supporting Statement describing the motivation to apply to the project, your CV and transcripts of awarded and in-progress university qualifications . Please note late or incomplete applications will not be considered. Equality, diversity and inclusion are fundamental to the success of The University of Manchester and central to all our activities. A diverse research community strengthens creativity, productivity and quality, while increasing the societal and economic impact of our work. We welcome applicants from all career paths, backgrounds and sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation or transgender status. We welcome applications from candidates returning to study after a career break or experience in other roles. Flexible study arrangements may be available, including part-time study at 50%, 60% or 80%, subject to the requirements of the project and funder. Eligibility : The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD Computer Science, Artificial Intelligence, Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Mathematics, Biomedical Engineering or another relevant quantitative or life-science discipline (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD Computer Science, Artificial Intelligence, Data Science, Bioinformatics, Computational Biology, Genomics, Statistics, Mathematics, Biomedical Engineering or another relevant quantitative or life-science discipline (or international equivalent). Previous research experience, or professional experience, in one or more of the following areas is preferred: multi-omics data analysis, machine learning, statistical or generative modelling, longitudinal data analysis, causal inference, or biological network analysis. Relevant experience gained in biotechnology, pharmaceutical, healthcare-data or computational research settings would also be valuable. Familiarity with neuroscience or the biology of ageing is desirable but not essential. Applicants are not expected to have expertise across all listed areas. Candidates with a strong computational background and an interest in biology, or a strong biological background with demonstrated quantitative skills, are encouraged to apply. Applicants should have experience in programming and quantitative data analysis. This project will remain open until filled. If your application is submitted by 1 st November 2026, you can expect a decision by 18 th December 2026. If your application is submitted by 15 th January 2027, you can expect a decision by 30 th March 2027. Self or externally funded students can also be considered for this project. FSESoE

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