Research Assistant in Computational Epidemiology
2 weeks ago
Location: White City Campus
Job Summary
This job is an opportunity to join the School of Public Health, Department of Epidemiology and Biostatistics, Imperial College, and work in the Computational Epidemiology team of Prof M Chadeau-Hyam and work on the EU funded EXPANSE project. This is an exciting and innovative project that combines statistics, machine learning, environmental and social sciences as well as molecular medicine to investigate drivers of cardiometabolic health. Research focuses on integrating Exposome datasets featuring a large number of measurements and/or observations, to better understand the lifestyle, environmental, metabolic, and genetic causes of chronic disease, as well as understanding the features of the exposome that are driving the quality of ageing and individual risk of adverse conditions. This work will be done under the direct supervision of Prof M Chadeau-Hyam, leading the statistical workpackage of the project, and the post holder will report to Prof M Chadeau-Hyam.
Duties and responsibilities
The post-holder will be responsible for the development of advanced statistical models to identify biologically imprinted effects of external (blocks of) exposures, 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.
Other Duties
- To contribute to the work of research teams, and taking responsibility for the research items allocated
- To write reports for submission to research sponsors
- To present findings via publications to referred journals and to colleagues at conferences
- To provide guidance and collaborate with staff and students
- To attend relevant workshops and conferences as necessary
- To promote the reputation of the Group, the Department and the College
Essential requirements
- A sound understanding of machine learning algorithms, specifically applied to population health and epidemiology
- Familiarity with omics data analyses, including pre-processing algorithms, development of ad-hoc models accounting for technically–induced variation (e.g. laboratory artefacts)
- Capacity to develop and implement novel approaches/strategies to investigate how data arising from several omics platforms interplay and ultimately mediate disease development
- Experience in using multivariate models to screen in high-dimensional data
- Strong programming skills (R, Python) for the preparation of bespoke scripts to analyse and integrate large datasets, including computational skills to optimize and ensure scalability of the resulting models
- Demonstrated ability to interact with other academics
- Presentation of work at meetings and conferences
- Experience in using tools for interpretable machine learning approaches such as feature importance metrics
- Knowledge of R, Python
- Knowledge of basic research methods and statistical procedures
Further Information
The current post in full-time, fixed term for 12 months and you will be based at White City Campus.
Should you require any further details on the role please contact: Prof Marc Chadeau-Hyam m.chadeau@imperial.ac.uk
Hybrid working may be considered for this role. Staff working in roles that are suitable for hybrid working will normally be expected to work 60% of their time onsite. The opportunity for hybrid working will be discussed at interview.
More information is available on the following web page: Work Location Framework | Administration and support services | Imperial College London
Closing date: 26 June 2024
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