Research Assistant – Data Science

Great Ormond Street Institute of Child Health

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UCL, Gower Street, London, UK


  • Posted on: 18th Feb 2021
  • Salary: £31,542- £33,257 per annum
  • REF: 1874983
  • Closes: 04th March 2021
  • Contract Type: Fixed-Term/Contract
  • Hours: Full Time, Part Time

Duties and Responsibilities

The Institute of Child Health, within the Faculty of Population Health, wishes to appoint a Research Assistant Data Science for Project Fizzyo, a Rosetrees Stoneygate – funded project grant, supervised by Professor Eleanor Main (ICH). This project involves data analytics following – passive remote capture and transmission of daily longitudinal data on airway clearance techniques (ACTs) and activity monitoring for children with Cystic Fibrosis (using chipped devices and wearable activity trackers) and computer games to enhance ACTs engagement, driven by electronic signals generated during breathing.

This remote monitoring platform has the potential to guide personalised physiotherapy prescription based on individual treatment responses, generate accurate, sustainable population-based physiotherapy registry data, and provide a template for remote monitoring of other interventions in CF across the NHS.

The successful candidate will expect to use data from 100 children with CF (daily data captured over 18 months), reconciled with their demographic and longitudinal clinical records, and use big data analysis including regression modelling, data visualisation and machine learning techniques to identify positive and negative clinical outcome predictors. Computer gaming has been introduced and removed (interrupted time series design) to evaluate the effect of gaming on ACT adherence and clinical outcomes during the study. The post-holder will: develop methods to quantify relationships between clinical outcomes and adherence to airway clearance techniques (ACTs) and exercise in children with CF and develop sensitive composite outcome measures generated from big data analysis for use in clinical management of children with CF and take responsibility for key components of the research, including the selection of research questions, development of new approaches, and preparation of papers for academic publications. You will work with data from diverse structured and unstructured data sources in both batch and streaming modes, and various formats including tabular and time series and work on all aspects of the design, development and delivery of deep learning enabled solutions, including problem definition, data acquisition, exploration, training, testing and evaluating deep learning models.

This post is funded for 8 months. Further funding to support the post may be available.

Key Requirements

The successful candidate will demonstrate extensive expertise in programming and data science and will have been awarded an MSc in a relevant area. Deep ML background and relevant experience with at least one of the ML or big data related technologies (Spark, Hive HQL, SAS, SPSS, RevR, Azure ML, MapR), Relevant Experience in programming and data prep with industry standard tools, especially R, Python, Javascript etc. Experience of computer software and tools for example; scripting language (APIs, Matlab, Python), Querying Language (SQL), using Git and GitHub, Spreadsheet (Excel) and Statistical Language (eg R, SPSS).

Further Details

A job description and person specification can be accessed at the bottom of the page.

To apply for the vacancy please click on the Apply Now button below.

If you have any queries regarding the application process, please contact Madhur Sharma on quoting job reference

UCL Taking Action for Equality

We will consider applications to work on a part-time, flexible and job share basis wherever possible.

Closing Date

4 Mar 2021

Latest time for the submission of applications


Interview date


Our department holds an Athena SWAN Gold award, in recognition of our long-term commitment and ‘beacon’ status in advancing gender equality.

This appointment is subject to UCL Terms and Conditions of Service for Research and Support Staff.

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