Data Assimilation Scientist

Your role

The scientist recruited for this role will be responsible for adapting and deploying state-of-the-art data assimilation methodologies used in the NWP workflow towards the specific needs and requirements of future reanalysis systems. Concurrently, he/she will take the lead in developing bespoke solutions for reanalysis data assimilation when these methods are not yet mature. 

A specific focus of the role is towards the development, adaptation and extension of the ECMWF variational and ensemble-variational DA systems to increase their skill and reduce their computational costs when deployed in the future C3S reanalysis framework. The objective is to better exploit the capabilities of 4D-Var and the ECMWF Ensemble of Data Assimilations (EDA) system to achieve a step change in the accuracy and fidelity of future reanalysis products while reducing overall computational costs. 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 and will work in close collaboration with colleagues from the C3S Reanalysis Team. 

About ECMWF

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 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 (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 

About the Earth System Assimilation and Copernicus C3S

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) in the framework of the Copernicus Climate Change Service (C3S). 
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 both for NWP and the generation of reanalysis products. Increasingly, Machine Learning technologies are being integrated into the standard DA development workflows. 

The Copernicus Climate Change Service (C3S) develops, maintains and safeguards the quality of state-of-the-art global climate reanalysis produced at ECMWF. The latest state-of-the-art ERA5 reanalysis is a C3S flagship product which provides a gapless hourly product from 1940 onwards for many quantities of the atmosphere, land surface and ocean surface. Preparations for the next-generation reanalysis ERA6 are under way.

Main duties and key responsibilities

• Develop and implement scientific and technical innovations in the ECMWF 4D-Var - based assimilation system and its EDA component to improve reanalysis accuracy and fidelity 
• Further develop and improve methodologies for uncertainty estimation and modelling in the assimilation cycle, including Machine Learning solutions
• Explore and develop innovative solutions for the improved representation of large scale circulation properties and constraints which are important for the identification of climate trends
• Contribute to the maintenance and support of the DA and ensemble DA systems in reanalysis 

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 equivalent industry experience

Experience required in the following areas:

• Experience of development of data assimilation systems for Numerical Weather Prediction or other environmental applications is essential 
• Experience of scientific software development on High Performance Computing systems is desirable
• Experience in the use of Machine Learning technologies  is desirable

Knowledge and skills required:

• In depth understanding of data assimilation methodologies and techniques
• General knowledge of meteorology and/or climatology and/or 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.

Candidates must be able to work effectively in English . A good knowledge of one of the Centre’s other working languages (French or German) is an advantage.

Other information

Grade remuneration    The successful candidate will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. ECMWF also offers a generous benefits package, including a flexible teleworking policy. 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 www.ecmwf.int/en/about/jobs, including the ECMWF Staff Regulations and the terms and conditions of employment.

Starting date:               From 01 May 2025

Contract duration:      Approx. 3.5 years to 30 Sept 2028

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

As a multi-site organisation, ECMWF has adopted a hybrid working model that 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

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 Cooperating States, listed below, as well as from all EU Member States:

ECMWF Member and Co-operating States are: Austria, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Hungary, Germany, Georgia, Greece, Iceland, Ireland, Israel, Italy, Latvia, Lithuania, Luxembourg, Montenegro, Morocco, the Netherlands, Norway, North Macedonia, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey 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.