Research Fellow in Climate Change and Health Data & Statistics
UCL - Institute for Global Health (IGH)
| Location: | London, Hybrid |
|---|---|
| Salary: | £43,374 to £51,860 |
| Hours: | Full Time |
| Contract Type: | Fixed-Term/Contract |
| Placed On: | 17th March 2025 |
|---|---|
| Closes: | 7th April 2025 |
| Job Ref: | B02-08506 |
About us
The UCL Institute for Global Health (IGH) is at the heart of UCL’s Grand Challenge of Global Health. The Institute’s vision is for a world where international policy on global health is informed by world-class research. IGH aims to support the development of robust solutions to aspects of the world’s major global health challenges through scholarly outputs, education, public engagement, translational research, and ultimately by influencing public policy and professional practice.
We are seeking a Research Fellow in Climate Change and Health to work on the coordination and timely delivery of the research and academic initiatives of the Lancet Countdown, including the annual Global Lancet Countdown Report, Regional Reports, special issues, and additional academic and research outputs.
About the role
The successful candidate will have strong quantitative and data analysis skills, ideally with expertise in climate and geospatial data; will contribute to the Lanet Countdown’s research on climate change and health; and will lead on data management, data translation activities, and the programme of open data and data sharing for the Lancet Countdown.
This role offers the opportunity for an early career researcher to work with over 300 researchers that contribute to the Lancet Countdown and to develop and expand a research portfolio in the field of climate change and health in support of the overall Lancet Countdown aims and personal career development.
About you
The post holder will have a PhD or equivalent working experience in climate science, epidemiology, statistics, data science, or related field with strong quantitative methods and data analysis.
It is essential that the successful candidate has advanced knowledge of quantitative methods and statistics, advanced proficiency in R or a similar coding language and experience in the analysis of geospacial data.
For further information about this role please visit UCL Careers website.
Customer advert reference: B02-08506
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