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Research Associate / Research Fellow (Advancing understanding of Multimorbidity in Metabolic Disease through Innovation in Statistical Machine Learning) (3 Posts Available)

The University of Manchester - Biology, Medicine & Health

Location: Manchester
Salary: £33,309 to £40,927 (Research Associate) and £42,149 to £51,799 (Research Fellow) per annum, depending on relevant experience
Hours: Full Time
Contract Type: Fixed-Term/Contract
Placed On: 7th October 2021
Closes: 3rd November 2021
Job Ref: BMH-017626

Job reference: BMH-017626

Location: Oxford Road, Manchester

Closing date (DD/MM/YYYY): 03/11/2021

Salary: £33,309 to £40,927 (Research Associate) and £42,149 to £51,799 (Research Fellow) per annum, depending on relevant experience

Employment type: Fixed Term

Faculty/Organisation: Biology, Medicine & Health

School/ Directorate: Informatics, Imaging & Data Sciences

Hours per week: Full time

Contract Duration: Starting as soon as possible, 2 x posts for 36 months and 1 x post for 24 months

The University of Manchester delivers a world-leading portfolio of health informatics research and training across the North of England and beyond. An opportunity has arisen for multiple Research Associates and/or Fellows to join the Centre of Health Informatics (CHI) and join the project entitled “Advancing understanding of multimorbidity in metabolic disease through innovation in analytical methods”. This project is a collaboration between the University of Manchester, University of Oxford, and Novo Nordisk. The project aims to develop and apply novel statistical and machine learning methods for prediction and treatment of multi-morbidity, with particular focus on metabolic disease. 

This is an exciting opportunity for experienced statisticians and data scientists with a strong interest in the health domain. The successful candidates will be responsible for the development of novel statistical and machine learning methods and for applying these methods to real-world electronic health records (e.g. the Wales Multimorbidity Cohort) in the context of metabolic disease. You will be based at the University of Manchester, working under the guidance from Professor Niels Peek, Dr Matthew Sperrin, and Dr Glen Martin, and also work closely with colleagues in Oxford (Professor Chris Holmes) and Copenhagen (Dr Kajsa Kvist, Dr Trine Abrahamsen).

The roles will be particularly appealing to anyone who has an interest in developing /applying novel data science methods to solve real-world clinical questions. You should have (i) a PhD (or equivalent) in Statistics, Health Informatics, Data Science, or related fields; (ii) previous experience of applying statistical modelling and/or machine learning to routinely collected healthcare data (ideally with experience of developing novel methods to solve a given problem/task); and (iii) experience of R programming for statistics/data science, ideally in the area of prediction and/or causal modelling.

The School/Department is strongly committed to promoting equality and diversity, including the Athena SWAN charter for gender equality in higher education. The School/Department holds a Silver Award which recognises their good practice in relation to gender; including flexible working arrangements, family-friendly policies, and support to allow staff achieve a good work-life balance. We particularly welcome applications from women for this post. All appointment will be made on merit. For further information, please visit:

Our University is positive about flexible working – you can find out more here

Blended working arrangements may be considered.

Please note that we are unable to respond to enquiries, accept CVs or applications from Recruitment Agencies.

Enquiries about the vacancy, shortlisting and interviews:

Name: Prof Niels Peek  


General enquiries:


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This vacancy will close for applications at midnight on the closing date.

Further particulars including job description and person specification are available on the University of Manchester website - click on the 'Apply' button above to find out more

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