Chancellor’s Fellow in Climate Science and Data Science at the University of Edinburgh

Please see for details of the Chancellor’s Fellowship scheme at the University of Edinburgh and the application process. The College of Science and Engineering is seeking to appoint up to ten Chancellor’s Fellows who have a strong track record in research (with at least two years at postdoctoral level, which may include innovation and/or impact), and the demonstrated potential to make a leading contribution to the University through furthering the strategic goals of the host School and College. We strongly encourage applications from women and individuals from black and minority ethnic groups. We also are very open to applications from those with non-traditional career paths, including those with family, caring or health career breaks, or those who have moved to academia from a career in another sector.

In particular, the School of GeoSciences proposes to recruit for a Chancellor’s Fellow in the area of climate science and data science. Data science and machine learning shows promise to revolutionise sciences ability to draw knowledge from increasingly large datasets, from high resolution satellite retrievals to kilometre-scale climate and earth system models. Many key processes occur on scales that evade the standard models and the arrival of much higher resolution will enable better constraints at the process level, yet exceeds the capability of standard methods. Machine learning is being developed and applied for parameterizations, causal inference including nonlinear pathways, emulators, detection of extreme events and early warning systems for tipping points and abrupt transitions in the climate system or the earth system (including humans) in response to climate change. Such a position could provide synergy and vital links with the following areas:

  • activity in atmospheric, cryospheric and climate science in GeoSciences, where many members of staff have some pilot projects and studentships that involve machine learning
  • the wider community in GeoSciences in earth observation, many of which experiment with and employ machine learning methods
  • mathematics and statistics to which many of the methods relate to, particularly the more statistically oriented ones the School of Informatics where there is vital expertise on this
  • EPCC (Edinburgh Parallel Computing Centre)
  • the Bayes Centre due to links to large data approaches
  • and the wider College of Science & Engineering where this topic is a growth topic.

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