Postdoctoral Research Associate/Fellow

The Faculty wishes to recruit a Postdoctoral Research Associate/Fellow to support the work of Prof David Stephenson. This NERC funded post is available for 2 years from 1 October 2025. The successful applicant will contribute to the research activities of the 4SEE project, which examines the so-called ‘signal-to-noise paradox’ in climate predictions and projections.  

In seasonal climate prediction, numerical climate models are used to forecast the evolution of the atmosphere and ocean for the next few months. These models produce forecasts of North Atlantic and European climate that, on average, better predict the real world than themselves. Given that models and initial conditions are imperfect, one might expect the opposite. This has become known as the signal-to-noise paradox (SNP). The SNP is interpreted as an underestimation of the predictable signal in models, but its root cause remains a mystery. The post will contribute to the 4SEE project which aims to identify the physical causes of the SNP and develop pathways to eradicate it.

The successful applicant will work under the supervision of Prof David Stephenson, Prof James Screen and Prof Adam Scaife and join a large team of world-leading scientists including at the Met Office. The successful applicant will develop and rigorously test new statistical methods to diagnose the SNP, including parametric and non-parametric approaches. Once optimal approaches have been found, they will be applied to large ensembles of climate predictions. They will develop continuous-in-time coupled time-series models and apply them to observations and models to diagnose the causes of weak predictable signals, and to recalibrate long-term climate projections. 

About you

The successful applicant will have relevant experience that involves mathematical development and application of statistical models. They will be able to develop and apply statistical models, to efficiently analyse large data sets from climate model ensembles, requiring competency in scientific programming and data visualisation, and to write papers for high-impact scientific journals.

For Grade E

The successful applicant will be able to present information on research progress and outcomes, communicate complex information, orally, in writing and electronically and prepare proposals and applications to external bodies.

Applicants will possess a relevant PhD (or nearing completion) or possess an equivalent qualification/experience in a related field of study and be able to demonstrate sufficient knowledge in the discipline and of research methods and techniques to work within established research programmes.

For Grade F

The successful applicant will be able to develop research objectives, projects and proposals; identify sources of research funding and contribute to the process of securing funds and make presentations at conferences and other events.

Applicants will possess a relevant PhD or equivalent qualification/experience in a related field of study. The successful applicant will be a nationally recognised authority in statistics and possess sufficient specialist knowledge in the discipline to develop research programmes and methodologies. The successful applicant will also be able to work collaboratively, supervise the work of others and act as team leader as required.

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