Scientist for Machine Learning - Atmospheric Composition

The Role

We are looking for a talented Machine Learning Scientist to spearhead exploratory development of an AIFS atmospheric composition forecasting model. The successful candidate will be embedded into both CAMS and AIFS teams and be supported by domain experts from both disciplines. They will explore how the AIFS should be adapted and trained to leverage atmospheric composition datasets, particularly analysis and reanalysis datasets. They will be at the forefront of understanding the role of machine learning for atmospheric composition forecasting. 

The Challenge

ECMWF is building a world-leading, machine learning based probabilistic weather forecasting system (AIFS), to complement our existing physics-based system (IFS). We are pioneering the operationalization of machine learning forecasting models in this domain. ECMWF now runs both deterministic and probabilistic AIFS forecasts daily, providing open data and products to users around the world. 

Global atmospheric composition forecasting with the IFS is also a key service of ECMWF. The forecasts are a core component of the Copernicus Atmosphere Monitoring Service (CAMS). The IFS is today one of the most comprehensive and advanced systems in the world for data assimilation and forecasting of global atmospheric composition, with operational outputs serving hundreds of users daily and being seen by millions (for instance through CNN, Windy, and other platforms). Early results in the scientific literature suggest that data-driven forecasting could be a valuable contribution to the operational forecasting of atmospheric composition. 

ECMWF will expand the scope of the AIFS and explore how data-driven forecasting can complement physical and chemical models in producing impactful operational forecasts of atmospheric composition. The operational CAMS forecasting system is used for the monitoring and forecasting of air quality, monitoring the ozone layer, and increasingly the monitoring of anthropogenic emissions of greenhouse gases. 

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 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. 

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. We appreciate the need for flexibility in the way our staff work. We have  adopted a hybrid work model that allows flexibility to staff to mix office working and teleworking, including away from the duty station for up to 10 days/month (within the area of our member states and co-operating states).

See   for more info about what we do.  

The Copernicus Atmosphere Monitoring Service 

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 Change and the Atmosphere Monitoring Services of the EU Copernicus 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. It is enabling ECMWF to now provide data and products covering weather, climate, air quality, fire and flood prediction and monitoring. 

The Copernicus Atmosphere Monitoring Service (CAMS) provides consistent and quality-controlled information related to air pollution and health, solar energy, greenhouse gases and climate forcing, everywhere in the world. For details, see . The question of the environmental impacts on human health has received growing interest in recent years, in particular during the COVID-19 crisis. The UN World Health Organisation estimates that over 7 million people worldwide (over 400.000 in Europe) die prematurely due to insufficient air quality. Improving the estimates of the exposure of populations to the main pollutants is a key objective for CAMS in order to increase awareness and support the development of more protective public policies. Another key topical area of CAMS is the estimation of emissions of greenhouse gases using observations in the atmosphere: this is essential to quantify the effectiveness of mitigation policies and guide the development of new ones at city, regional and country level. 

Main duties and key responsibilities

• Defining and building atmospheric composition training datasets
• Contributing to AIFS/Anemoi codebase developments to expand functionality to support atmospheric composition modelling
• Training AIFS atmospheric composition models
• Contributing to the evaluation of AIFS atmospheric composition models
• Provide technical and scientific input to user support and training activities that are within the expertise of the successful applicant
• Provide technical management of related external CAMS contracts

What we are looking for

• Excellent analytical and problem-solving skills with a proactive and constructive approach
• Flexibility, with the ability to adapt to changing priorities
• Ability to work autonomously and as part of multidisciplinary and geographically distributed teams
• Excellent interpersonal and communication skills
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines
• Genuine interest in ECMWF and Copernicus, especially on atmospheric composition monitoring and forecasting

Education

• Advanced university degree (EQ7 level or above) or equivalent professional experience in computer science or engineering, computational science, physics or natural sciences, mathematics, or a related discipline

Experience, Knowledge and Skills 

• Experience developing and training large-scale neural networks in PyTorch (or similar framework)
• Experience in spatial-temporal modelling with neural networks would be desirable
• Experience with atmospheric composition modelling would be an advantage
• Experience developing with Python and software lifecycle maintenance is required
• Experience contributing to open-source or large-scale projects, involving multiple software components would be an advantage
• Experience with geospatial data handling with Python would be an advantage

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 candidates will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. 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 .

Starting date:      as soon as possible

Candidates are expected to relocate to the duty station. 

Interviews will be conducted by videoconference (MS Teams).

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, as well as from all EU Member States.  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. 

ECMWF Member States 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.