[ad_1] Location: St Mary’s Campus (Paddington) Job Summary This job is an opportunity to join the School of Public Health, Department
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Location: St Mary’s Campus (Paddington)
Job Summary
This job is an opportunity to join the School of Public Health, Department of Epidemiology and Biostatistics, Imperial College, and work within the multidisciplinary Computational Epidemiology group of Prof M Chadeau-Hyam and work on the EU funded LongITools project.
This is an exciting and innovative project that combines statistics, machine learning, environmental and social sciences as well as molecular medicine to investigate lifecourse Exposome drivers of health and ageing. Research focuses on integrating Exposome datasets featuring a large number of measurements and/or observations, to better understand the lifestyle, environmental, causes of chronic disease and their biological imprints. The work aims at better 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 and Dr Dragana Vuckovic, leading the statistical workpackage of the project.
You will report to Prof M Chadeau-Hyam, will have the opportunity to contribute to other large-scale international projects involving the group and will have the opportunity to contribute to teaching and supervision of MSc students from the Helat Data analytics and Machine Learning programme.
Duties and responsibilities
You will be responsible for the development of advanced statistical models and machine learning algorithms to identify (i) biologically imprinted effects of external (blocks of) 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.
Essential requirements
You should have a PhD in statistics, epidemiology or any related discipline.
Constructing and applying 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:
- Preparation of bespoke scripts
- Integrating omics and other data in the lifecourse
- Biologically interpret results.
Successful applicants will 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
Further Information
This is a full time, fixed term position for 16 months. You will be based at St Mary’s Campus, Paddington.
Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £38,194 – £41,388 per annum.
Should you require any further details on the role please contact: Prof. Marc Chadeau-Hyam – m.chadeau@imperial.ac.uk.
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