Research Associate in Medical Statistics

We are seeking a Research Associate in Medical Statistics to join a project entitled “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. Additionally, we want to maximise engagement and information obtained from digital remote monitoring by asking the right questions, at the right frequency and time.

The post holder’s main duty will be to provide statistical expertise 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), and will consider statistical and machine learning algorithms.
2. Development of statistical methods for adaptive sampling. At present, we collect real-time data from patients at regular intervals. We would like the digital system to monitor user data in real-time and learn to dynamically adjust the sampling frequency. For example, changes in patterns of behaviour from passive data may be an early warning of deteriorating symptoms and can be used by adaptive sampling models to increase the frequency of symptom collection. Frequency can be reduced if a person appears to be stable and well. You will investigate methodology to allow this, and investigate/compare them using the data from the CONNECT study.

You will join an engaged 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.

You should have a PhD (or equivalent) in statistics or a similar field, and be developing your publication record. You should have specific skills and expertise in statistical modelling and/or machine learning.

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

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/

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: Dr Glen Martin

Email: glen.martin@manchester.ac.uk

General enquiries:

Email: People.recruitment@manchester.ac.uk

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.