Research Associate in Biostatistics and Machine Learning

Job summary

We are excited to announce that we are looking for a Research Associate in Biostatistics and Machine Learning to join a multi-disciplinary team. This is a great opportunity for a researcher with a strong statistical background to join our team of biostatisticians, epidemiologists and exposure scientists, to develop and apply statistical methods in for spatial and temporal structured data in the context of air pollution source characterisation...

Job listing information

  • Reference MED02245
  • Date posted 29 January 2021
  • Closing date 1 March 2021

Job description

Job summary

We are excited to announce that we are looking for a Research Associate in Biostatistics and Machine Learning to join a multi-disciplinary team. This is a great opportunity for a researcher with a strong statistical background to join our team of biostatisticians, epidemiologists and exposure scientists, to develop and apply statistical methods in for spatial and temporal structured data in the context of air pollution source characterisation and health effect evaluation.

This post is part of the project titled: “A statistical framework for the apportionment of particulate contaminants and their health effect determination”, funded by the Medical Research Council.

Duties and responsibilities

You will develop mixture models and other machine learning techniques to cluster particulate matter components into their sources and then link these sources to health outcomes. You will use a combination of simulations and real case studies throughout the project which will inform policy makers on the differential health impact of air pollution sources. The research team includes statisticians and exposure scientists from the MRC Centre for Environment and Health, as well as an epidemiologist at Public Health England, making the project closely linked to policy making.

Key responsibilities will include:

  • Developing mixture models in a Bayesian nonparametric framework that fit dependent data in time or space, explicitly including covariates that can affect the source specification (e.g. meteorology, geographical coordinates).
  • Extending the source apportionment model to evaluate the link between sources and health outcomes and predict the health risks under different scenarios of changes in the air contaminants.
  • Building simulation studies to assess the performance of the developed approach in comparison with state-of-the-art tools for source apportionment and for their health effect evaluation.

Essential requirements

You should have a thorough working knowledge of modern applied statistical techniques, including Bayesian hierarchical modelling and machine learning methods, preferably covering Bayesian nonparametric models. You should be highly proficient in R, preferably having used Rstan or R-nimble packages and hold a PhD in Statistics or a closely related quantitative discipline. A good understanding of environmental epidemiological concepts and techniques that apply to a wide range of study designs is essential for this role.

Further information

The post is offered on full time, fixed term basis for 32 months.

Candidates who have not yet been officially awarded their PhD will be appointed as a Research Assistant within the salary range £36,045 - £39,183 per annum.

For informal enquiries please contact Prof Marta Blangiardo: m.blangiardo@imperial.ac.uk.

For technical issues when applying online please email support.jobs@imperial.ac.uk

The College is a proud signatory to the San-Francisco Declaration on Research Assessment (DORA), which means that in hiring and promotion decisions, we evaluate applicants on the quality of their work, not the journal impact factor where it is published. For more information, see https://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-evaluation/

The College believes that the use of animals in research is vital to improve human and animal health and welfare. Animals may only be used in research programmes where their use is shown to be necessary for developing new treatments and making medical advances. Imperial is committed to ensuring that, in cases where this research is deemed essential, all animals in the College’s care are treated with full respect, and that all staff involved with this work show due consideration at every level. http://www.imperial.ac.uk/research-and-innovation/about-imperial-research/research-integrity/animal-research

Documents

About Imperial College London

Imperial College London is the UK’s only university focussed entirely on science, engineering, medicine and business and we are consistently rated in the top 10 universities in the world.

You will find our main London campus in South Kensington, with our hospital campuses located nearby in West and North London. We also have Silwood Park in Berkshire and state-of-the-art facilities in development at our major new campus in White City.

We work in a multidisciplinary and diverse community for education, research, translation and commercialisation, harnessing science and innovation to tackle the big global challenges our complex world faces.

It’s our mission to achieve enduring excellence in all that we do for the benefit of society – and we are looking for the most talented people to help us get there.

Additional information

Please note that job descriptions cannot be exhaustive and the post-holder may be required to undertake other duties, which are broadly in line with the above key responsibilities.

All Imperial employees are expected to follow the 7 principles of Imperial Expectations: 

  • Champion a positive approach to change and opportunity
  • Communicate regularly and effectively within, and across, teams
  • Consider the thoughts and expectations of others
  • Deliver positive outcomes
  • Encourage inclusive participation and eliminate discrimination
  • Develop and grow skills and expertise
  • Work in a planned and managed way 

In addition to the above, employees are required to observe and comply with all College policies and regulations.

We are committed to equality of opportunity, to eliminating discrimination and to creating an inclusive working environment for all. We therefore encourage candidates to apply irrespective of age, disability, marriage or civil partnership status, pregnancy or maternity, race, religion and belief, gender identity, sex, or sexual orientation. We are an Athena SWAN Silver Award winner, a Disability Confident Leader and a Stonewall Diversity Champion.

For technical issues when applying online please email support.jobs@imperial.ac.uk.