Research Associate in Machine Learning for Digital Phenotyping

Job reference: BMH-027375
Salary: £36,924 to £45,163 per annum, dependent on relevant experience
Faculty/Organisational unit: Biology, Medicine Health
Location: Oxford Road
Employment type: Fixed Term
Division/Team: Division of Informatics, Imaging & Data Science
Hours per week: Full Time (1 FTE)
Closing date (DD/MM/YYYY): 25/11/2024
Contract duration: 2 years (24 months)
School/Directorate: School of Health Sciences

We are seeking a Research Associate in Machine Learning and Digital Phenotyping to work across multiple projects. The first project is “CONNECT: Digital markers to predict psychosis relapse”. This project will recruit individuals with psychosis and use smart phone apps to collect passive and active data using a prospective observational cohort study design. We will use this data to develop and validate a personalised risk prediction algorithm for relapse. The second project is the Mental Health Mission, a £42m UK Government investment into new infrastructure for mental health. The data and digital theme is focussed on developing new digital phenotypes from multiple forms of data including electronic health records, smartphones and wearables. The aim is to develop transdiagnostic digital biomarkers from patient generated health data.

The postholder’s main duty will be to provide machine learning in the study, with responsibility for:

1. Development, and validation, of a risk prediction model for relapse. Using data from the prospective study, you will develop/train a risk prediction model that can be used to identify patients at risk of relapse. This will use data from a variety of sources including symptoms recorded in real-time through a mobile phone app, and passively collected data such as geolocation. The model will be dynamic (updating predictions as patients record new data). An experimental approach will be taken to explore different predictors and machine learning methods.
2. Development of digital phenotypes. You will use emerging data from CONNECT to identify digital phenotypes and different clusters of phenotypes and how they predict relapse. By linking the CONNECT data to electronic health records you will develop extended digital phenotypes.
3. Develop novel approaches to standardising digital biomarkers from patient generated data. An objective of the group is to develop repeatable approaches to extracting digital phenotypes form patient generated data where there is heterogeneity across devices used to generate and collect data.

You must have a PhD (or equivalent) in artificial intelligence, and be developing your publication record. You must have specific skills and expertise in applied machine learning to healthcare problems

The School of Health Sciences 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: www.bmh.manchester.ac.uk/about/equality

What you will 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.           

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor John Ainsworth

Email: john.ainsworth@manchester.ac.uk

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