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.
A job description and person specification can be accessed at the bottom of the page.
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4 Mar 2021
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