Postdoctoral Research Associate in Solar Radiation Management

Full-time: 35 hours per week

Fixed-term: for 24 months, with start date of 1^st September 2026, or as soon as possible thereafter

The University of Edinburgh is a world-class, research-intensive institution. We offer a supportive working environment, excellent facilities, and opportunities for professional development within a diverse and international community.

The Opportunity:

This is an Academic Research (Postdoctoral Research Associate) opportunity within the School of GeoSciences.

We seek a postdoctoral research associate (PDRA) for a 24-month position to work on the UK NERC-funded grant “Quantifying Efficacy and risks of solar radiation management (SRM) approaches using natural analogues”. The project will use novel machine learning-based methods to determine the climate response to a range of natural event (e.g. wildfires, volcanic eruptions) which can be used as analogues of SRM to provide evidence for informing model improvement without worry about the risks of field experiments. The derived large-scale observational constraints will be used to constrain and advance climate models, and to attribute climate response to SRM.

This position will target a critical problem in climate modelling to improve cloud scheme that contributes one of the most important uncertainties in climate projections (Wang et al 2026, Nature Communications). Wang et al (2026) found that climate models largely underestimated cloud cover response to aerosol perturbation, pointing a key direction for improving climate projections. You will work closely with a Scientist at ETH Zurich to apply a new Neural Network-Based Cloud Fraction scheme into UKESM (UK Earth System Model) and design sensitivity experiments to test its effect, and lead the further improvement of cloud fraction scheme for climate simulations.

This post is full-time (35 hours per week), however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.

Your skills and attributes for success:

• PhD in a highly quantitative subject, such as, but not limited to, atmospheric or climate science, meteorology, physics, earth observation.
• Expertise in working with climate modelling, ideally experience with model development with FORTRAN.
• Good programming skills in a data processing and visualization language such as Python, MATLAB, R or NCL.
• Proven ability to work both independently and as part of a team.
• Experience of disseminating findings through scientific publications and conference presentations.

Informal enquires can be made to Dr. Yu Wang: y.w@ed.ac.uk

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