Research Associate (Fixed Term)

An opportunity has arisen for a talented statistician or probabilistic machine learning methods developer to join Dr Paul Kirk's group at the MRC Biostatistics Unit, Cambridge University, as part of the MIMAH (Minimising Mortality from Alcoholic Hepatitis) Consortium; see also: www.mimah.org.

Alcohol related liver disease (ALD) is responsible for more than 6000 deaths a year in the UK and costs the NHS £3.5 billion. Alcoholic hepatitis is a florid presentation of ALD in which patients present with jaundice and liver failure.  Unfortunately, around 30% of people admitted to hospital with this condition will die within 3 months.  The treatment of alcoholic hepatitis is complicated by the fact that there is inflammation within the liver whilst the patient is very susceptible to infection. As a result, treatment with drugs, such as steroids, which suppress the immune system may exacerbate the risk of infection.

We will apply state of the art Bayesian clustering and machine learning approaches to analyse data collected within the MIMAH consortium and as part of the STOPAH trial; see also www.mimah.org/biostatistics.

We are seeking an ambitious and motivated individual to contribute to this research. The team has strong collaborations with groups across the Cambridge Biomedical Campus and beyond. Current team members are working on unsupervised, semi-supervised and outcome-guided clustering and integrative clustering, multi-view modelling, multiple kernel learning, Bayesian nonparametrics, and matrix factorisation approaches, across an array of applications.

The successful candidate will have a PhD in a strongly quantitative discipline, ideally statistics or probabilistic machine learning. Experience with biomedical applications would be highly advantageous, but not essential. A desire to address questions of substantive biological importance and disease relevance is essential. Good communication skills and an enthusiasm for collaborating with others are also essential. Strong programming ability would be desirable, and experience of Bayesian clustering and variable selection approaches would be advantageous. Experience with clinical and molecular datasets would be highly desirable. The successful applicant will be supported in their career development with a range of formal courses and on-the-job training.

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 Unit is actively seeking to increase diversity among its staff, including promoting an equitable representation of men and women. The Unit therefore especially encourages applications from women, from minority ethnic groups and from those with non-standard career paths. Appointment will be made on merit.

For an informal discussion about this post please contact paul.kirk@mrc-bsu.cam.ac.uk

Fixed-term: The funds for this post are available until 30^th May 2024 in the first instance.

To apply online for this vacancy and to view further information about the role, please click the apply button above.

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: 22^nd October 2023

The interview date for the role is: To be confirmed

Please quote reference SL38702 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.