Scientist to work on machine learning for environmental modelling

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The Scientist will work in the Innovation Platform Team in the Forecast Department at ECMWF and will be responsible for developing new methods to upscale flood and fire forecast model system from catchment to European scale using machine learning techniques. The work will contribute to the H2020 project EIFFEL (rEvealIng the role oF geoss as the deFault digital portal for building climatE change adaptation & mitigation appLications) and will utilize the forecast model systems used in EFFIS and EFAS within the Copernicus Emergency Management System in combination with GEOSS and Copernicus data to improve the operational model output. The Scientist will apply semantic machine-learning tools developed in the project to augment the forecast systems and assess potential improvements on the European scale. Such improvements can for example, involve improving background maps, incorporating processes such as irrigation or land use change, as well as improving the forcing observational data. They will also create impact response surfaces (IRS) over Europe using the model system to assess the sensitivity of the models with and without the augmentation. The IRS will then be used in the project for climate change impact assessment.

The successful applicant will be responsible for implementing the developed methodology in ECMWF’s IT infrastructure and report the output in deliverables to the project as well as in international scientific journals. They will work closely with other teams in the Forecast Department responsible for the CEMS flood and fire services. They will also attend the EIFFEL workshops and project meetings and interact with the other project partners to contribute to a successful project. This role will also involve interacting with ECMWF’s group on artificial intelligence to assess the wider use of semantic machine learning methods in other parts of ECMWF’s systems.