Data Assimilation Scientist

The scientist recruited for this role will be responsible for developing and maintaining the variational and ensemble based ECMWF data assimilation systems. The successful candidate is expected to take an active role in exploring and implementing new scientific ideas and technologies to advance DA science and their application to ECMWF Earth Data Assimilation system. A specific focus of the role is towards the continuous development of the ECMWF variational ensemble DA system and its extension to coupled Earth system components. The objective is to fully characterise the uncertainties in the assimilation cycle and use this information to improve the accuracy of the analyses and the skill of the forecasts.
This development work will take place using both established variational/optimal estimation technologies and emerging machine learning methodologies.

Together with algorithmic developments, the role involves coding them into the ECMWF Integrated Forecasting System on a High Performance Parallel Computing infrastructure. The successful candidate will embrace the technical complexities of the job and be alert to the opportunities of the rapidly evolving computing infrastructure.

The scientists will be based in the Data Assimilation Methodologies team within the ESAS Section.

The Earth System Assimilation Section (ESAS) forms part of ECMWF’s Research Department. It develops and maintains state-of-the-art data assimilation techniques and infrastructure to bring together information from the forecast model and the global satellite and in-situ observation network to support the ECMWF numerical prediction systems. Activity covers all components of the Earth System (atmosphere, land, ocean and cryosphere) with the primary focus of improving the accuracy of weather forecasts. The techniques and infrastructure developed in ESAS are also being applied for environmental monitoring and prediction (e.g. atmospheric composition) and the generation of climate reference datasets (reanalyses).
Inside ESAS, the Data Assimilation Methodologies (DA) Team maintains and continuously develops the variational and ensemble-based assimilation infrastructure that is common to all the data assimilation activities at ECMWF. Increasingly, Machine Learning technologies are being integrated into the standard DA development workflows.

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 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.

ECMWF has 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 (HPC) 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.

For additional detail about ECMWF, see www.ecmwf.int

• Initiate, develop and implement scientific and technical innovations in the ECMWF 4D-Var - based assimilation system and its ensemble DA component
• Further develop and improve methodologies for uncertainty estimation and modelling in the assimilation cycle, including Machine Learning solutions
• Contribute to the maintenance and support of the operational DA and ensemble DA systems

• Excellent interpersonal and communication skills
• Strong analytical problem-solving and scientific curiosity
• Highly motivated to inspire scientific and technical innovation
• Dedication and enthusiasm to lead and work in a team
• Ability to work efficiently and complete a diverse range of tasks in a timely manner

• A university degree (EQF Level 8 or above) or equivalent industry experience

• Experience of development of data assimilation systems for Numerical Weather Prediction or other environmental applications
• Experience of scientific software development on High Performance Computing systems

• In depth understanding of data assimilation methodologies and techniques
• General knowledge of meteorology and Earth System science
• Proficiency in scientific computing (Fortran, C++, python, code and workflow management systems)
• Knowledge and experience in developing machine learning applications would be a plus
• Scientific planning, reporting and communication (written and verbal)
• Candidates must be able to work effectively in English and interviews will be conducted in English.

We encourage you to apply even if you don’t feel you meet precisely all these criteria.

Grade remuneration: The successful candidate will be recruited at the A2/A3 grade, depending on relevant experience, according to the scales of the Co-ordinated Organisations. ECMWF stated salaries are the NET annual basic salary and we also offer a generous benefits package, including a flexible teleworking policy. The position is assigned to the employment category STF-C as defined in the ECMWF Staff Regulations. Full details of salary scales and allowances plus the ECMWF Staff Regulations and the terms and conditions of employment, are available on the ECMWF website at www.ecmwf.int/en/about/jobs.

Starting date: From 01 March 2025

Length of contract: 4 years with the possibility of further contracts

Location: Reading, UK or Bonn, Germany (Candidates are expected to relocate to the duty station)

As a multi-site organisation, ECMWF has adopted a hybrid organisation model which allows flexibility to staff to mix office working and teleworking. We allow for remote work 10 days/month away from the office, including up to 80 days/year away from the duty station country (within the area of our member states and co-operating states).

Successful applicants and members of their family forming part of their households will be exempt from immigration restrictions.

Interviews will take place via videoconference (MS Team). If you require any special accommodations in order to participate fully in our recruitment process, please contact us via email: jobs@ecmwf.int

Applicants are invited to complete the online application form by clicking on theapplybutton 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.

ECMWF Member 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.

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.