Research Fellow in Spatial Data Science (Public Health)

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Research Fellow in Spatial Data Science (Public Health), London

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Client:

Location:

London, United Kingdom

Job Category:

-

EU work permit required:

Yes

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Job Reference:

4556e6c6d0e9

Job Views:

4

Posted:

29.04.2025

Expiry Date:

13.06.2025

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Job Description:

About the role

This research role is on the project PREDICT – which brings together clinicians and data scientists from Barts Health and Barts Life Sciences, natural language processing specialists Clinithink, and experts in urban analytics from University College London. Barts aims to reduce life-threatening illnesses caused by undiagnosed heart valve disease, to redress health inequalities and to reduce the suffering and costs of heart valve disease through earlier detection. Barts NHS Trust is developing a community-facing service for heart tests, which need to be better targeted. The main research task of the UCL team will be working on the work packages, including Social Geography Mapping of NE London Valvular Risk and better detection of valve diseases. The role requires close collaboration with academics at the Centre of Advanced Spatial Analysis (Dr Chen Zhong) and the Department of Geography (Dr Stephen Law) at UCL, as well as industrial partners from Barts. Duties include:

• Reviewing the latest literature on health populations and data-driven, machine-learning methods applied to improve health services;
• Constructing a semantically enriched social and environmental health determinants database from open sources and integrating it with clinical data from Barts;
• Constructing spatial regression and machine learning models to predict patients at risk of undiagnosed heart valve disease;
• Visualising outcomes through geographical mapping;
• Assisting Barts's research team in exploring multi-modal data science approaches to establish culturally appropriate community diagnostic hubs;
• Communicating effectively with academics, clinical and non-clinical staff, and the public;
• Participating in meetings with UCL colleagues and the project consortium;
• Writing academic papers for conferences and journals in collaboration with UCL and Barts colleagues;
• Sharing academic outputs via presentations, conferences, and public engagement events;
• Adhering to research ethics, data security, storage, and protection guidelines.

The position is available from 1 July, funded until 30 June initially. The starting salary ranges from £43,000 to £49,000 per annum, including London Allowance. Appointment at Grade 7 requires a PhD; otherwise, initial appointment at Grade 6B (£38,000–£41,000) with backdated Grade 7 payment upon PhD completion. The role is subject to UCL Terms and Conditions. Applications for part-time, flexible, and job-share arrangements are welcomed. For queries, contact Chen Zhong. A detailed job description and person specification are available at the bottom of the page. To apply, click the ‘Apply Now’ button below.

About you

The ideal candidate will have a PhD (or near completion) in geography, GIS, spatial data science, computer science, or engineering. Essential criteria include:

• Knowledge of programming languages such as Python or R for spatial data analysis and modeling;
• Understanding of health geography research challenges;
• Familiarity with geographic datasets and skills in manipulating, analyzing, and visualizing them;
• Expertise in spatial data science methods, clustering, regression, optimization, and machine learning;
• Ability to design and conduct quantitative urban and health geography research;
• Strong communication skills for diverse audiences;
• Proven publication record in peer-reviewed journals and conferences;
• A positive, flexible attitude and organizational skills to manage workload and meet deadlines.

See the full list of criteria in the detailed job description and person specification.

What we offer

Benefits include:

• 41 days holiday (27 days annual leave, 8 bank holidays, 6 closure days);
• Additional 5 days’ annual leave purchase scheme;
• Defined benefit pension scheme (CARE);
• Cycle to work scheme and season ticket loan;
• Relocation scheme for eligible posts;
• On-site nursery and gym;
• Enhanced maternity, paternity, and adoption pay;
• Employee assistance programme;
• Discounted medical insurance.