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Exploring new methodology in Data Analytics to advance population health


Project Description

Summary
1 fully funded, 3.5 year PhD studentship is available to a highly motivated UK or EU student to work within a centre of excellence in the linkage and analysis of data across disciplines. The successful applicant will be trained to be part of the next generation of data scientists and develop skills and know how to deliver on the integration of diverse clinical data types – from molecules to man to populations.

Project Description
In recent years, the health sector has seen a significant increase in the volume and diversity of data that is gathered and stored on a daily basis. Ranging from administrative data, clinical observations and patient level attributes, through to epidemiological, omics data and images, the data is frequently siloed, often in different organisations, and analysed in the absence of the other data streams relating to that individual. To take advantage of the growing availability of these data types, a more comprehensive, systems modelling approach needs to be applied to account for multiple dimensions, integration of diverse data types, and changes over time.
We are looking for an applicant to work on a project in Population Health, linking datasets at population level and addressing issues of national and international importance, such as modelling pathways to antimicrobial resistance and integrating health and social care. You will explore and advance new methodology in Statistics and Data Analytics, develop new visualisation and data analysis tools that are capable of harnessing the full potential of these diverse and large data sets. As a PhD candidate, you will sit within the Mathematics and Statistics department of the University of Strathclyde but will have equal supervision from the Business School. As a Strathclyde University PhD candidate, you will have access to the Strathclyde Researcher Development programme (PG Cert) offering you a competitive advantage as a research professional.

Supervisor Webpages
https://www.strath.ac.uk/staff/barrysarahdr/
https://www.strath.ac.uk/staff/megiddoitamarmr/

Keywords: big data, machine learning, statistics, health informatics, epidemiology, population health

Further Information:

How to Apply:
Applicants must have obtained, or expect to obtain, a first or 2.1 UK honours degree, or equivalent for degrees obtained outside the UK, in a quantitative or scientific discipline.

Please direct all enquiries and applications to or . Applications will be reviewed when received, shortlisted candidates will be invited to interview on a rolling basis and it is anticipated that PhD Studentships will start in October 2019. The application process will remain open until the position is filled.

All applications must be submitted via email (subject line: PhD applicant – University of Strathclyde STRADDLE DTC) as a single pdf file and include the following:
1) A cover letter (max 1 page) explaining your interest and fit to the DTC programme
2) A CV (maximum three pages)


Funding Notes

Funding will cover full tuition fees for UK/EU candidates, and provide a tax-free PhD stipend in the region of £14,000 per annum. Please note that candidates outside the EU will not be eligible for funding under this studentship.

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