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ECMWF

The European Centre for Medium-Range Weather Forecasts (ECMWF) is an independent intergovernmental organisation supported by 34 states. ECMWF is both a research institute and a 24/7 operational service, producing and disseminating numerical weather predictions to its Member States. This data is fully available to the national meteorological services in the Member States. The Centre also offers a catalogue of forecast data that can be purchased by businesses worldwide and other commercial customers. The supercomputer facility (and associated data archive) at ECMWF is one of the largest of its type in Europe and Member States can use 25% of its capacity for their own purposes. The organisation was established in 1975 and now employs around 300 staff from more than 30 countries. ECMWF is one of the six members of the Co-ordinated Organisations, which also include the North Atlantic Treaty Organisation (NATO), the Council of Europe (CoE), the European Space Agency (ESA), the Organisation for Economic Co-operation and Development (OECD), and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).

05/06/2025
Full time
Scientist for Machine Learning (Atmospheric Composition)
ECMWF Bonn, Germany
The role We are looking for a talented Machine Learning Scientist or Atmospheric Composition Scientist with AI/ML experience to contribute to the use of AI/ML in atmospheric composition monitoring and forecasting. This includes the exploratory development AIFS-COMPO, the use of AI/ML based emulators of the relevant chemical and physical processes in IFS-COMPO, and the potential application of AI/ML for the global monitoring of emissions and surface fluxes. As a   Scientist for Machine Learning (A2)   you will be embedded into both CAMS and AIFS teams and be supported by domain experts from both disciplines. You will explore how the AIFS or elements of the traditional IFS-COMPO should be adapted and trained to leverage atmospheric composition datasets, such as analysis and reanalysis datasets, as well as how these new techniques could be used for the monitoring of emissions and natural fluxes. You will be at the forefront of understanding the role of machine learning...