This is an exciting opportunity for an ambitious researcher to join an internationally renowned research Unit and a team carrying out high profile work at the cutting edge of development and application of Bayesian methods to infectious disease epidemiology.

The post-holder will be part of Professor De Angelis' team within the Population Health Theme at the MRC Biostatistics Unit (MRC-BSU). The project, funded by a Wellcome Discovery award in collaboration with the Universities of Oxford and Manchester and the UK Health Security Agency, aims to develop new methods to understand community transmission of respiratory infections using data from the Office of National Statistics (ONS) COVID-19 Infection Survey (CIS).

The ONS CIS is a unique household survey with longitudinal follow up of over 500,000 participants who were regularly swabbed for SARS-CoV-2 infection at over 10 million visits, with positive swabs sent for whole-genome deep sequencing. A subset of participants were additionally tested for RSV and influenza. Over 100,000 high-quality SARS-CoV-2 sequences are available, linked to data on behaviour, symptoms and vaccination status collected at each visit. The ONS CIS was regularly used to estimate prevalence of SARS-CoV-2 in the population to inform policy during the pandemic (e.g. https://bit.ly/4bL6mvu). However, these data remain under-utilised.

The post-holder will exploit the opportunity offered by the ONS CIS data collection to better understand community infection and transmission. Starting with SARS-CoV-2, the main task is to develop new methods to estimate incidence and transmission, harnessing the rich epidemiological and genomic data available. This will allow quantification of changes in transmission across pandemic waves while disentangling the contribution of different viral lineages. An important constraint in this project is the need for individual-level data to remain within a computationally limited secure research environment, so use of computationally efficient approaches will be key. Outputs will include new estimation tools and inform the design of future population surveys, contributing to pandemic preparedness agenda.

The successful candidate will have a PhD in a strongly quantitative discipline, but applications from candidates close to submitting their PhD are also welcome. Research experience in Bayesian statistics is essential as is experience in statistical programming, an ability to produce high-quality academic writing, good communication skills and an enthusiasm for collaborating with non-statistical scientists. Experience in infectious disease transmission modelling is highly advantageous. The successful candidate will be supported in their career development with formal courses and on-the-job training.

MRC-BSU is one of Europe's leading biostatistics research institutions. Our focus is to develop and apply new analytical and computational strategies for the challenging tasks facing biomedicine and public health. The Unit is situated on the Cambridge Biomedical Campus, one of the world's most vibrant centres of biomedical research, which includes the University of Cambridge's Clinical School, two major hospitals, the MRC Laboratory of Molecular Biology, and the world headquarters of Astra Zeneca.

The Biostatistics Unit is committed to supporting hybrid working for all staff, but we do expect that staff will work from the office on a regular basis to help integration and to build our exceptional scientific community. Working entirely from the office is possible.

Fixed-term: The funds for this post are available for 4 years in the first instance.

To apply go to https://www.jobs.cam.ac.uk/job/46622/ Click the 'Apply' button to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload a covering letter and a CV in the Upload section of the online application. The covering letter should outline how you match the criteria for the post and why you are applying for this role. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please include details of your referees, including email address and phone number, one of which must be your most recent line manager.

The closing date for applications is: 24th June 2024

The interview date for the role is: To be confirmed

Please quote reference SL41767 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.