Scientists for data driven Earth system modelling in Destination Earth

Your role 

We are searching for highly motivated Scientists to build machine learned models for the ocean, ocean waves, sea ice and the land surface that can be used to improve the Digital Twins of Destination Earth (DestinE), for numerical weather predictions, and for climate modelling. 

At ECMWF, you will find a passionate community, collectively aiming to build the best global Earth system models for numerical weather prediction and climate simulations. ECMWF has been the first operational weather centre to publish results of their own global machine learned weather model – the Artificial Intelligence Forecasting System (AIFS) – that are continuously updated with the latest predictions on their webpage . Within DestinE, ECMWF will now develop and deploy workflows of machine learned Earth system components of a European foundation model based on existing traditional simulation and modelling results from DestinE. The successful applicants will work jointly to develop ocean, sea-ice, and land components, as well as an interactive representation of ocean waves, which are not covered by the atmospheric AIFS. The different domain areas will be covered by the three positions and it is not expected that a single applicant will cover all topics. The positions will be filled with domain scientists that will be part of the Land and Ocean Modelling Teams, that have originally focussed on conventional modelling. However, the work will be performed closely together with a group of machine learning scientists who will support the developments from a data-science perspective. This will enable to build the best possible models that exploit the full potential of machine learning and high-performance computing while still being tested and evaluated by domain scientists who check for physical consistency and limits in predictability. The model configurations will be tested for simulations with forecast lead-times up to a couple of weeks. The developed tools will be closely integrated into the DestinE workflow but will also be considered for use in operational weather predictions at ECMWF. This effort supports ECMWF’s strategy of producing cutting‐edge science and world-leading weather predictions and monitoring of the Earth system.

About the Land Modelling and Ocean Modelling Teams

The teams are responsible for the representation of the land surface including urban areas and hydrology, as well as ocean, sea ice, and ocean wind waves within the forecast model of ECMWF’s Integrated Forecasting System (IFS). Both teams are situated in the Earth System Modelling Section that leads the developments of the forecast model within ECMWF’s Department of Research. The contracts are funded by and support the Destination Earth initiative, which also links with activities at ECMWF Member States and our partners at ESA and EUMETSAT.

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 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.

See   for more info about what we do. 

The Destination Earth (DestinE) Initiative 

ECMWF is one of the three entities entrusted to implement the Destination Earth (DestinE) initiative of the European Commission, alongside ESA and EUMETSAT as partners. DestinE aims to deploy several highly accurate thematic digital replicas of the Earth, called Digital Twins. The Digital Twins will help monitor and predict environmental change and human impact, in order to develop and test scenarios that would support sustainable development and corresponding European policies for the Green Deal.  ECMWF is responsible for the delivery of these digital twins and of the Digital Twin engine, the software infrastructure needed to power them of some of Europe’s largest supercomputers, those of the European HPC Joint Undertaking (EuroHPC). 

The second phase of DestinE covers the period June 2024 – May 2026, and future phases are foreseen (subject to funding). Phase 2 will focus on early operations with consolidation, maintenance, and continuous evolution of the DestinE system components developed in the first phase. There will also be an enhanced focus on ML activities, including the deployment of workflows of components of a ML model for the Earth system, optimisation of the Digital Twin Engine to enable efficient model training and simulations, and other activities. 

One key element of the ML activities in phase 2 includes training. This shall build on recent ML training initiatives at ECMWF, including the MOOC on ML for Weather and Climate (see https://learning.ecmwf.int/course/index.php?categoryid=1).

For more information on DestinE, see https://ec.europa.eu/digital-single-market/en/destination-earth-destine and https://www.ecmwf.int/en/about/what-we-do/environmental-services/destination-earth 

Your responsibilities 

• Develop training datasets for ocean, waves, sea-ice and land from model output, including DestinE Digital Twins, analysis and reanalysis, and observations that can be used to train global machine learning models
• Turn the forecasting problem into a data-science problem including the generation of input and output data but also a learning task with an appropriate loss function to build trustworthy and competitive ocean, wave, sea-ice and land models
• Together with machine learning scientists, develop an efficient training workflow that makes efficient use of the available compute power.
• Adjust the training workflow, diagnostics and loss functions to achieve the optimal predictions across forecast lead times
• Diagnose model performance and physical consistency of the trained machine learning models for medium-range numerical weather forecasts and extended range predictions, and compare model quality with results from the conventional models

What we're looking for

• Proven analytical and problem-solving skills with a proactive approach to improve models and tools
• Excellent interpersonal and communication skills
• On the one hand self-motivated and able to work with minimal supervision, on the other hand dedicated and enthusiastic about teamwork with willingness to work in close collaboration with other scientists, teams and sections at ECMWF
• Ability to maintain effective communication and documentation regarding model and project developments and the support of the research-to-operation process
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines

Education

• An advanced university degree (EQF Level 7 or above) or equivalent professional experience

Experience

• Experience in either ocean, wave, sea-ice or land modelling for weather and climate prediction are required
• Experience to work with either physical or machine learning models in the domain of weather and climate prediction are required
• Experience in machine learning within the area of weather and climate science would be an advantage
• Experience with creating ocean, wave, sea-ice or land datasets from model output, analysis or reanalysis, and observations would be an advantage

Knowledge and skills

• Expert knowledge in either ocean, wave, sea-ice or land modelling 
• Good knowledge in numerical schemes and algorithms for numerical weather prediction models or related fields
• Good knowledge about machine learning and in particular deep learning is desirable
• Candidates must be able to work effectively in English

Other information 

Grade remuneration:  The successful candidates will be recruited at Grade A2, according to the scales of the Co-ordinated Organisations and the annual basic salary will be GBP 71,451 (Reading/UK) or EUR 86,824 (Bonn/Germany) NET annual basic salary (ECMWF salaries are exempt of national income tax). In addition to basic salary, ECMWF also offers an attractive package of benefits and entitlements. This position is assigned to the employment category STF-PS  as defined in the ECMWF Staff Regulations. To find out more about working with us and for full details of salary scales and allowances, please visit . 

Starting date:                As soon as possible

Length of contract:     The contract duration is expected to be two years up to 31 May 2026.  There may be the possibility of further contract extensions in the future, depending on requirements and funding availability.

Location:                         Reading, UK or Bonn, Germany

Remote work:            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 by videoconference (MS Teams) are expected to take place during February-March 2024. If you require any special accommodations in order to participate fully in our recruitment process, please let us know. 

To contact the ECMWF Recruitment Team, please 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 Co-operating States, 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, 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, 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.