Scientist for Machine Learning

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Organisation: ECMWF


ECMWF is the leading centre for global, medium-range weather predictions and is the host of the largest archive of weather data in the world. ECMWF is both a research institute and a 24/7 operational service, producing and disseminating numerical weather predictions to its Member States. ECMWF has been also entrusted to operate the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S) on behalf of the European Commission. Every day, hundreds of millions of satellite and in situ observations are processed at ECMWF to provide the basis for up-to-date global analyses and climate reanalyses of the atmosphere, ocean and land surface, and to generate global weather predictions from hours up to a year ahead. To retain its world-leading position, ECMWF is performing cutting edge research in weather related sciences and high-performance computing. ECMWF’s weather forecasts are disseminated to the ECMWF Member States and thousands of users around the world.

Summary of the role

ECMWF has embarked on an exciting new initiative to explore the use of artificial intelligence and machine learning in applications of numerical weather predictions. To learn more about the application of machine learning in the weather and climate domain and at ECMWF, please have a look at the webpage of the Machine Learning Seminar Series at ECMWF or the ESA/ECMWF machine learning workshop which is planned for October.

As part of this effort, ECMWF is coordinating the MAchinE Learning for Scalable meTeoROlogy and climate (MAELSTROM) EuroHPC project to fund this position. This Scientist position will be in the Physical Processes team in the Research department at ECMWF. The successful candidate will apply their skills, knowledge and expertise to help achieving the goals, and complete the deliverables, of the MAELSTROM project.

The main focus will be on the development of machine learning emulators for some of the parameterisation schemes of ECMWF’s Integrated Forecast System (IFS). The use of deep learning to emulate some of the parametrizations used to represent subgrid atmospheric processes, such as radiation or clouds, promises a significant reduction of computing cost and improvements in portability of the models to heterogeneous hardware, and could potentially lead to improvements in predictive skill if savings are reinvested into higher resolution or model complexity. This project builds on previous studies which have successfully used neural network emulators within weather and climate simulations. However, the use in a forecasting system for operational weather predictions, such as the IFS, will require more complex machine learning solutions and training data of higher quality than what has been used so far. The successful candidate will also explore the use of such emulators within the data assimilation framework.

The Scientist will work in close collaboration with other teams across the organisation and strong communication skills are essential.