Research Associate in Causal Machine Learning

We are seeking a Research Associate with expertise in machine learning and causal inference to join the University of Manchester spoke of “CHAI hub: Causality in Healthcare and AI”. The CHAI hub; led by University of Edinburgh and involving University College London (UCL), Imperial College, Kings College London, and University of Exeter; is a £12M investment in the development of new theory and methods for causality as applied to healthcare.

At University of Manchester, our focus in the hub is on how we can develop and apply causal inference for decision support. Therefore, we are interested in methods to calculate individualised treatment effects, and to allow for predicting future outcomes under a range of interventions. We are also interested in how the incorporation of causal inference in clinical prediction models can improve generalisability and transportability and ensure fairness.

Specific exemplars of interest include optimising cancer treatment including how to optimise radiotherapy treatment plans; cancer diagnosis and screening where fairness of models that prioritise individuals for screening is a particular concern; and cardiovascular primary prevention where we seek to optimise both population-level and individual-level intervention, providing guidance both to individuals and policymakers.

You should ideally have expertise in causal inference and come from a statistical and/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 to make a real difference in the field of causal AI. In CHAI we are focusing on flagship projects that require collaboration across all the centres. Extensive training and mentoring opportunities are provided.

You will also join an engaged data science community at Manchester with over 400 investigators working across the University in different disciplines allied to data sciences and connected through the Institute for Data Science and Artificial Intelligence. Our expertise covers the complete data science life cycle: from information management, through analytics, to practical applications. A key feature of our approach is very close coupling between methodologists and translational scientists, drawing on strength-in-depth in real-world applications of data science. This creates a virtuous circle, where challenging real-world problems drive the methodology research agenda, whilst providing a natural route to exploiting new algorithms and methods. We believe this deeply multidisciplinary approach is one of the distinctive features of data science at Manchester.

The school is strongly committed to promoting equality and diversity, including the Athena SWAN charter for gender equality in higher education. The school 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. An appointment will always be made on merit. For further information, please visit: https://www.bmh.manchester.ac.uk/about/equality/

What will you get in return:

• 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 over the Christmas period
• Local and national discounts at a range of major retailers

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

Hybrid working arrangements may be considered.

Please be aware that due to the number of applications we are unfortunately not able to provide individual feedback on your application.

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

Any recruitment enquiries from recruitment agencies should be directed to .

Any CV’s submitted by a recruitment agency will be considered a gift.

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor Matthew Sperrin

Email:

General enquiries:

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Technical support:

https://jobseekersupport.jobtrain.co.uk/support/home

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

Please see the link below for the Further Particulars document which contains the person specification criteria.