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 .
About you
Applicants will need a PhD degree in statistics or a related discipline (machine learning, mathematics, data science), as well as some knowledge of either causal inference or machine learning. Previous research experience in a health-related area would be an advantage.
What we offer
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 insurance
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About the roleThe 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...
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