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 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.

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.