Research Associate in Computational Epidemiology
The research programme in Computational Epidemiology led by Prof Chadeau-Hyam focuses on the development and application of novel statistical and machine learning approaches to explore the contribution of internal and external components of the exposome to cardiometabolic health. This project will integrate (possibly time-resolved) external exposome features in millions of European citizens and high-resolution molecular profiles in subsamples...
Job listing information
- Reference MED02093
- Date posted 22 October 2020
- Closing date 5 November 2020
The research programme in Computational Epidemiology led by Prof Chadeau-Hyam focuses on the development and application of novel statistical and machine learning approaches to explore the contribution of internal and external components of the exposome to cardiometabolic health. This project will integrate (possibly time-resolved) external exposome features in millions of European citizens and high-resolution molecular profiles in subsamples of these study populations to evaluate their marginal and joint predictive and explanatory performances.
The post is funded by the EU H2020 project ‘EXPANSE’ in close collaboration with the Utrecht University, IINSERM and ovder 10 leading universities in Europe and the US (Columbia, and Mount Sinai, NYC). The project aims to extend the Exposome paradigm into a population science framework. Specifically, the project will combine data from administrative cohorts and matured adult cohort (totalling tens of millions of individiuals) whose environment will be described and their environmental exposures estimated. From these the project will identify sets of exposures that are contrubting to population stratification, and will subsequently identify internal/molecular signatures of such exposures. In a final step the project wills investigate the marginal and joint effect of these external and internal features on cardiometabolic outcomes. Within EXPANSE, Prof. Chadeau-Hyam leads the statistical Work Package and has responsibility to to develop approaches to identify Expotypes, there internal signatures and (internal/external) exposome risk scores.
Duties and responsibilities
The post holder will develop and apply (in collaboration with all project partners) statistical models to (i) identify cluster of individuals sharing similar exposomes, (ii) identify which exposome features mostly contribute to the population stratification, (iii) identify (multi-) omics signatures of these external features, and (iv) assess the performances of both internal and external exposome features in explaining the risk of cardio-metabolic conditions.
Due to the nature of the project we expect the applicant to have broad experience across several fields. The successful applicant will have a strong background in molecular epidemiology and the application of advanced statistical models. A good understanding of the importance the environment (including social factors) and their possible biological and molecular processes is essential. A sound understanding of epidemiological concepts particularly in relation to molecular epidemiology and strong methodological skills, including experience in analysing large-scale and high dimensional data using best practice in research software engineering are also essential.
We expect the post-holder to carry out, plan and lead high a high-quality programme of research in computational epidemiology, undertake project management and supervise multi-disciplinary teams. In addition, the Fellow will be expected to submit publications to refereed journals and to attract external research funding.
The post is full time and fixed term for 18 months.
Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £35,477 - £38,566 per annum.
Should you require any further details on the role please contact: Marc Chadeau (firstname.lastname@example.org)
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