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The position will develop cutting-edge machine learning applications to support future climate projections. This will allow you to apply and extend the latest generative machine learning approaches and train these at scale on very large datasets. Simultaneously, you will help to define the state-of-the-art in Earth system modelling and support Europe’s efforts to better understand and adapt to climate change.
Machine learning techniques for weather forecasting have made tremendous progress in the last two years, with state-of-the-art models now providing skill comparable to the best equation-based models at a fraction of the compute costs. For climate, large-scale machine learning techniques are still in their infancy and have so far mainly played a supporting role. The open position gives you the possibility to contribute to the development of large generative machine learning for climate applications. The positions is funded by two related projects, WarmWorld ICON-Rep and EXPECT.
The WarmWorld ICON-Rep project will extend the AtmoRep model (https://github.com/clessig/atmorep), a large-scale, self-supervised representation learning model for atmospheric dynamics. In particular, you will enable native support for ICON mesh data in AtmoRep and than train the adapted model on ICON climate simulations using large scale compute infrastructure, including JUPITER – the first European exa-scale supercomputer at the Jülich Supercomputing Center. With the trained model, it will be explored to what extent an interpolation or extrapolation of climate scenarios is possible, e.g. if training on a small number of CMIP scenarios will allow to generate samples also from other ones not seen during training. Such a capability would hold enormous potential to speed-up and improve climate projections.
The EXPECT project develops European climate change projection and attribution capabilities. The position will support this by implementing a novel downscaling model that can take low-resolution climate model simulation data as input and produce high-resolution data that provides climate information at the local scale where most adaptation and mitigation efforts take place. The localized data should be statistically consistent with high-resolution climate simulation, e.g. from the EERIE project (https://eerie-project.eu), the Destination Earth Climate Digital Two and NextGEMS (https://nextgems-h2020.eu) but be produced at a small fraction of the computational costs.
The position gives you the possibility to be part of and shape the exciting developments on machine learning for Earth System Modelling that are currently taking place at ECMWF (e.g. https://www.ecmwf.int/en/about/media-centre/aifs-blog), in the WarmWorld and EXPECT projects, and the wider community.
The position will be part of the Earth System Modelling section at ECMWF and tightly linked to the different machine learning efforts at the Centre, e.g. the AIFS, ECMWF’s data-driven forecasting model and the WeatherGenerator project.
The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world-leader in weather and environmental forecasting. As an international organisation we serve our members and the wider community with global weather predictions and data that is critical for understanding and solving the climate crisis. We function as a 24/7 research and operational centre with a focus on medium and long-range predictions, holding one of the largest meteorological data archives in the world. The success of our activities builds on the talent of our scientists and experts, strong partnerships with 35 Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies and machine learning across our operations.
ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Destination Earth Initiative and the Climate Change and Atmosphere Monitoring Services of the Copernicus Programme. Other areas of work include High Performance Computing and the development of digital tools that enable ECMWF to extend provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.
ECMWF is a multi-site organisation, with a main office in Reading, UK, a data centre/ supercomputer in Bologna, Italy, and a large presence in Bonn, Germany. We appreciate the need for flexibility in the way our staff work. We have adopted a hybrid work model that allows flexibility to staff to mix office working and teleworking, including away from the duty station for up to 10 days/month (within the area of our member states and co-operating states).
See for more info about what we do.
The WarmWorld project (https://www.warmworld.de) develops the next generation of climate models and the required computational infrastructure for this, funded by the German federal ministry for education and research. The ICON-Rep project, which is part of the WarmWorld Smarter stream, adapts the existing AtmoRep model for WarmWorld data and applications. ICON-Rep-Data focuses on the model development and climate scenarios. The sister project ICON-Rep-Data targets the compression of high-resolution data using AtmoRep, with both models being linked through the use of AtmoRep.
The EXPECT project develops a prototype operational capability for integrated attribution and prediction of climate phenomena by exploiting novel data and technologies to provide trustworthy assessments and predictions of regional climate change including extremes, funded by the European Union through a Horizon project. The open position supports the project through a machine learning-based downscaling application.
ICON-Rep:
EXPECT:
Education
Experience, Knowledge and Skills
We encourage you to apply even if you don’t feel you meet precisely all these criteria.
Grade remuneration: The successful candidates will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. The position is assigned to the employment category STF-PL as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances available on the ECMWF website at .
Starting date: as soon as possible
Candidates are expected to relocate to the duty station.
Interviews will be conducted by videoconference (MS Teams).
Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.
Applicants are invited to complete the online application form by clicking on the apply button below.
At ECMWF, we consider an inclusive environment as key for our success. We are dedicated to ensuring a workplace that embraces diversity and provides equal opportunities for all, without distinction as to race, gender, age, marital status, social status, disability, sexual orientation, religion, personality, ethnicity and culture. We value the benefits derived from a diverse workforce and are committed to having staff that reflect the diversity of the countries that are part of our community, in an environment that nurtures equality and inclusion.
Applications are invited from nationals from ECMWF Member States and Co-operating States, as well as from all EU Member States. In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy. Applications from nationals from other countries may be considered in exceptional cases.
ECMWF Member States and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Georgia, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye and the United Kingdom.
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