PhD Studentship - Sharing Reproducible Analyses in Big Healthcare Data Infrastructures

How to apply: www.manchester.ac.uk/study/postgraduate-research/admissions/how-to-apply

This funding is available for UK students, and EU students with settled status, at the current UKRI rate (tax free stipend of £18,622 and tuition fees).

Health Data Research UK (HDR UK) is a national institute dedicated to improving health outcomes and advancing medical research through the use of data. Established in 2018, HDR UK is a partnership between leading universities, research institutions, and the National Health Service (NHS) in the UK. The primary mission of HDR UK is to harness the power of health data to drive scientific discoveries, develop innovative treatments and interventions, and ultimately improve patient care. By integrating and analysing vast amounts of health data, including electronic health records, genomic data, and other relevant information, HDR UK aims to generate insights that can transform healthcare delivery, policy, and research.

The University of Manchester, the University of Nottingham and the University of Dundee have established a collaborative network to explore innovative ways to use health data for research and healthcare improvement. These initiatives involve developing new analytical techniques, data linkage methods, and tools for data sharing while ensuring patient privacy and data security. This funded PhD is an opportunity for training and collaboration with a wide network of researchers within those institutions.

In particular, at the University of Manchester, we want to better understand the challenges of computational reproducibility and FAIR data sharing within HDR UK federated data infrastructures, especially focussing on technology potential, limitations, integrity measures and handling of sensitive data.

We are looking to recruit a PhD student with an interest in developing and applying emerging approaches for federated computational analytics (e.g. scientific workflow systems) and metadata management across distributed systems.

Project objectives:

The project will aim to meet the following objectives. These are broadly outlined below, and will be finalised and made more specific during the PhD.

• Systematic review exploring existing literature on workflow management systems and methods to capture federated provenance and structured metadata
• Systematic review limitations, opportunities, ethical and privacy concerns that restricts FAIR sharing of sensitive healthcare data, identifying potential countermeasures
• Prototyping of a system that can filter federated workflow provenance to make it publishable as open datasets
• Development of framework for open sharing of provenance for sensitive data analysis
• Development of metadata models for mapping sensitive data identifiers 

Further Information:

You will work with Carole Goble (Professor of Computer Science) and Stian Soiland-Reyes (Research Fellow) in the eScience Lab at The University of Manchester; they both have a long track record of international research and development of scientific workflow systems, provenance standards and FAIR data sharing practices across life sciences.

You may be expected to participate in some HDR UK activities along with the other PhD studentship recipients.

Qualifications:

Applicants should hold a 2:1 undergraduate degree or better, and a masters degree in Computer Science or a subject relevant to one of data science, information modelling, Web technologies, Linked Data, knowledge graphs, or health care data processing; or equivalent international qualifications. 

Contact: 

Carole Goble (carole.goble@manchester.ac.uk), Stian Soiland-Reyes (soiland-reyes@manchester.ac.uk) use subject "HDR QQ2"

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