Research Fellow in causal inference and machine learning

at  University College London

ABOUT US

The Department of Statistical Science at UCL is the longest established university statistics department in the world and has played a pioneering role in the development of the subject since its foundation in 1911. It is one of nine departments in the UCL Faculty of Mathematical and Physical Sciences and has close links with many other departments, both within the Faculty and outside of it. We teach statistical science at all levels (undergraduate single/combined honours, service courses, MSc and PhD) and carry out research across a wide range of theoretical and applied areas. In the last Research Excellence Framework exercise (2021/22), over 97% of our output was classified as “world-leading” or “internationally excellent” in terms of originality, significance and rigour. We consistently score highly in the National Student Survey in the Postgraduate Taught Experience Survey.

ABOUT THE ROLE

The post is an exciting opportunity for a researcher with a strong background in causality in (bio)statistics or machine learning to undertake research combining state-of-the-art causal inference and machine learning approaches for treatment effect estimation and optimal treatment decision rules, using electronic health records (amongst other data sources).
This post is funded by a Royal Society-Wellcome Trust Sir Henry Dale Fellowship, awarded to Professor Karla DiazOrdaz. The Research Fellow will be part of a methodologically focused research group, with interactions with the Alan Turing Institute, Ghent University, and University of California Berkeley.
The post is available as soon as possible and it is funded for until 31st October 2026.

As well as the exciting opportunities this role presents, we also offer some great benefits some of which are:

• 41 Days holiday (27 days annual leave 8 bank holiday and 6 closure days)
• Additional 5 days’ annual leave purchase scheme
• Defined benefit career average revalued earnings pension scheme (CARE)
• Cycle to work scheme and season ticket loan
• Immigration loan
• Relocation scheme for certain posts
• On-Site nursery
• On-site gym
• Enhanced maternity, paternity and adoption pay
• Employee assistance programme: Staff Support Service
• Discounted medical insuranc