Job Description
Machine learning for weather and climate modelling is moving at a breath-taking pace. We are seeking exceptional individuals to undertake research into the development of machine learning based Earth System modelling approaches. Specifically this is your opportunity to revolutionise the approach of existing Earth System models in representing land surface hydrology and its feedbacks to the atmosphere, particularly at the extended range, and in providing accurate and reliable predictions of hydrological conditions and extremes such as floods.
This role is funded by the research programme on Advancing the Frontiers of Earth System Prediction (AFESP) - a £30million 15-year investment by the University of Reading, in partnership with the European Centre for Medium-Range Forecasts (ECMWF), the UK Met Office and the National Centre for Atmospheric Science. It will deliver sustained investments to tackle some of the far-term (10–15 year) and difficult (high-risk, high-reward) research challenges in global Earth System prediction.
By enhancing our capabilities in earth system modelling, the research programme will deliver a new class of accurate, reliable and usable forecasts, aiming to re-define the medium-range predictability limit from two to at least four weeks, enabling a wide range of new scientific and societal applications.
Full time, fixed term post for 5 years.
Interview date W/C18/03/2024
Main duties and responsibilities
Undertake collaborative research and make significant contributions to the following activities:
- Research that leverages the power of machine learning to improve the ECMWF Earth System Modelling approach. Specifically, to develop and test a novel hydrology-informed global foundation model for land surface hydrology and evaluate conceptual and scientific soundness using explainable AI techniques.
- Develop and evaluate fine-tuned models for land surface-atmosphere feedbacks at the extended range/S2S and fine-tuned models for forecasting floods at different spatial scales and lead times.
- Explore the potential of using these techniques to improve process representation, parameterisation and optimisation in ECMWF’s land surface model, ECLand and to explore the potential of adding a land surface hydrology foundation model to larger foundation models in weather and climate.
- Actively engage with the latest ML/AI techniques and apply them to earth system forecasting.
- Attend, contribute to, and organise relevant project meetings.
- Report on progress and results of the research through appropriate methods, including papers for submission to scientific journals, presentation of results at conferences and workshops, etc.
- Maintain awareness of current progress in relevant research areas to ensure that the research remains at the cutting edge.
- Liaise with collaborators from other parts of the programme on a regular basis, including travel to visit project partners as required.
Informal contact details | Alternative informal contact details | ||
Contact role: | Professor of Hydrology | Contact role: | Professor of Climate System Science & Climate Hazards and Director of University of Reading - ECMWF Research Collaboration |
Contact name: | Professor Hannah Cloke | Contact name: | Professor Pier Luigi Vidale |
Contact email: | h.l.cloke@reading.ac.uk | Contact email: | p.l.vidale@reading.ac.uk |
Applications from job seekers who require sponsorship to work in the UK are welcome and will be considered alongside all other applications. By reference to the applicable SOC code for this role, sponsorship may be possible under the Skilled Worker Route. Applicants should ensure that they are able to meet the points requirement under the PBS. There is further information about this on the UK Visas and Immigration Website.
The University is committed to having a diverse and inclusive workforce, supports the gender equality Athena SWAN Charter and the Race Equality Charter, and champions LGBT+ equality. Applications for job-share, part-time and flexible working arrangements are welcomed and will be considered in line with business needs.
