Data Assimilation Scientist

Your role 

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.

About the Earth System Assimilation/DA Methodologies Team

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.

About ECMWF 

ECMWF is the European Centre for Medium-Range Weather Forecasts. It is an intergovernmental organisation created in 1975 by a group of European nations and is today supported by 34 Member and Co-operating States, mostly in Europe. The Centre’s mission is to serve and support its Member and Co-operating States and the wider community by developing and providing world-leading global numerical weather prediction. ECMWF functions as a 24/7 research and operational centre with a focus on medium and long-range predictions and holds one of the largest meteorological archives in the world. The success of its activities relies primarily on the talent of its scientists, strong partnerships with its Member and Co-operating States and the international community, some of the most powerful supercomputers in the world, and the use of innovative technologies such as machine learning across its operations.

Over the years, ECMWF has also developed a strong partnership with the European Union, and for the past seven years has been an entrusted entity for the implementation and operation of the Climate and the Atmosphere Monitoring Services of the EU's Copernicus component of its Space Programme, as well as a contributor to the Copernicus Emergency Management Service. The collaboration does not stop there and includes other areas of work, including High Performance Computing and the development of digital tools that enable ECMWF to extend its provision of data and products covering weather, climate, air quality, fire and flood prediction and monitoring.
ECMWF has recently become a multi-site organisation, with its headquarters based since its creation in Reading, UK, its new data centre in Bologna, Italy, and new offices in Bonn, Germany.

For additional details, see www.ecmwf.int

Main duties and key responsibilities 

• 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

What we're looking for

• 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

Education

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

Experience required in the following areas

• 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

Knowledge and skills

• 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.

Other information 

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 available on the ECMWF website at www.ecmwf.int/en/about/jobs, including the ECMWF Staff Regulations and the terms and conditions of employment.

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).

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.

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

Who can apply 

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. 

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.