Job number
MED05388
Faculties
Faculty of Medicine
Departments
School of Public Health
Salary or Salary range
£43,863 - £47,223 per annum
Location/campus
White City Campus - Hybrid
Contract type work pattern
Full time - Fixed term
Posting End Date
22 Sep 2025
About the role
This job is an exciting opportunity to join the School of Public Health, Department of Epidemiology and Biostatistics at Imperial College, and work within the multidisciplinary Computational Epidemiology group of Prof M Chadeau-Hyam on the EU-funded STAGE project. This is an innovative project that combines statistics, machine learning, environmental and social sciences as well as molecular medicine to investigate ageing and its health outcomes. The work aims to better understand the features of the exposome that drive the quality of ageing and individual risk of adverse conditions. This work will be conducted under the direct supervision of Prof. M. Chadeau-Hyam and Dr Dragana Vuckovic, who will lead the statistical work package of the project.
What you would be doing
You will be responsible for the development of advanced statistical models and machine learning algorithms to identify (i) biologically imprinted effects of external exposures, (ii) their evolution in the life course and the contribution of other compartments of the exposome to these signals. Resulting statistical models should also facilitate the identification of molecular signatures of shared exposome types and their trajectories throughout the life course. The goal is to incorporate in high throughput profiling techniques, (i) both a causal and mechanistic component to explore mechanisms mediating the biological effect of individual experiences, and (ii) a longitudinal component to account for full history and for life stages at which individuals may be more susceptible or vulnerable.
You will report to Prof M Chadeau-Hyam and Dr D Vuckovic and you will have the opportunity to contribute to other large-scale international projects involving the group as well as to teaching and supervision of MSc students from the Health Data analytics and Machine Learning programme.
What we are looking for
The ideal candidate should have an MSc degree in health data science, computational epidemiology, biostatistics or a related discipline and be able to construct and apply statistical models using OMICs data, biochemistry and social factors in the life course in a longitudinal set up and investigate their effect on health and ageing; in particular:
* Prepare bespoke scripts
* Integrate omics and other data in the life course
* Biologically interpret results
Successful applicants will also have:
* A sound understanding of epidemiological concepts particularly in relation to molecular epidemiology
* Familiarity with omics data analyses, including pre-processing algorithms, development of ad-hoc models accounting for technically induced variation (e.g. laboratory artefacts)
* Experience in using multivariate models to screen in high-dimensional data
* Strong programming skills (R, C, C++) for the preparation of bespoke scripts to analyse and integrate full-resolution omics datasets, including computational skills to optimize and ensure scalability of the resulting models
* Experience in interpreting results from the analysis of OMICs data (e.g. genomics, epigenomics) using relevant software and visualisation tools
* capacity to develop and implement novel approaches/strategies to investigate how data arising from several omics platforms interplay and ultimately mediate the effect of external exposures
What we can offer you
* The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.
* The opportunity to become an active member of a multi-disciplinary and dynamic research group in Computational Epidemiology.
* The chance to collaborate with multiple European academic partners.
* Grow your career: gain access to Imperial's sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.
* Sector-leading salary and remuneration package (including 39 days off a year and generous pension schemes).
* Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.
Further information
This is a full-time time post and a fixed-term contract for 36 months – end date 30 Sept 2028.
If you require any further details about the role, please contact: Prof Marc Chadeau-Hyam, or Dr Dragana Vuckovic
Available documents
Attached documents are available under links. Clicking a document link will initialize its download.
Please note that job descriptions are not exhaustive, and you may be asked to take on additional duties that align with the key responsibilities mentioned above.
We reserve the right to close the advert prior to the closing date stated should we receive a high volume of applications. It is therefore advisable that you submit your application as early as possible to avoid disappointment.
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About Imperial
Welcome to Imperial, a global top ten university where scientific imagination leads to world-changing impact.
Join us and be part of something bigger. From global health to climate change, AI to business leadership, here at Imperial we navigate some of the world's toughest challenges. Whatever your role, your contribution will have a lasting impact.
As a member of our vibrant community of 22,000 students and 8,000 staff, you'll collaborate with passionate minds across nine London campuses and a global network.
This is your chance to help shape the future. We hope you'll join us at Imperial College London.
Our Culture
We work towards equality of opportunity, to eliminating discrimination, and to creating an inclusive working environment for all. We encourage applications from all backgrounds, communities and industries, and are committed to employing a team that has diverse skills, experiences and abilities. You can read more about our commitment on our webpages.
Our values are at the root of everything we do and everyone in our community is expected to demonstrate respect, collaboration, excellence, integrity, and innovation.