Research Associate in Causal Machine Learning

About the position

We are seeking a Research Associate with expertise in machine learning and causal inference to join the University of Manchester’s CHAI hub:
Causality in Healthcare and AI. The hub is a £12M investment led by the University of Edinburgh and involving University College London (UCL), Imperial College, King’s College London, and the University of Exeter. Our focus at Manchester is on developing and applying causal inference for decision support, including calculating individualized treatment effects and predicting future outcomes under a range of interventions.

We are interested in methods that improve generalisability, transportability, and fairness in clinical prediction models.

Specific exemplar projects include optimising cancer treatment (e.g., radiotherapy plans), cancer diagnosis and screening (with a focus on model fairness), and cardiovascular primary prevention (optimising population‑level and individual‑level interventions).

You should ideally have expertise in causal inference and come from a statistical or machine learning background. You will hold, or be about to obtain, a PhD in a relevant field. This is a unique opportunity to join a large UK‑wide team of experts in causal inference for healthcare and to make a real difference in the field of causal AI.

Extensive training and mentoring opportunities are provided.

The Manchester School of Medicine is strongly committed to promoting equality and diversity and holds a Silver Award for its Athena SWAN charter. We particularly welcome applications from women for this post. An appointment will always be made on merit.

• Fantastic market‑leading pension scheme
• Excellent employee health and wellbeing services, including an Employee Assistance Programme
• Exceptional starting annual leave entitlement, plus bank holidays
• Additional paid closure during the Christmas period
• Local and national discounts at a range of major retailers

• Seniority level:
  Internship
• Employment type:
  
  Full‑time
• Job function:
  Other
• Industry: Higher Education

Enquiries about the vacancy, shortlisting and interviews:
Name:
Professor Matthew Sperrin
Email: matt

General enquiries:
Email:
People.

Technical support:

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