Research Associate in Machine Learning for Digital Phenotyping

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 will join the digital phenotyping research group, led by Prof John Ainsworth. You will be part of the wider data science community at Manchester with of 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. Manchester is a member of The Alan Turing Institute: the UK’s national institute for data science, and researchers at Manchester lead a Turing research programme in health.

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: https://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.

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 People.Recruitment@manchester.ac.uk.

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

Enquiries about the vacancy, shortlisting and interviews:

Name: Professor John Ainsworth

Email: john.ainsworth@manchester.ac.uk

General enquiries:

Email: hrservices@manchester.ac.uk

Tel: 0161 275 4499

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.