Your role
We are looking for a highly motivated (Senior) Scientist to work on the representation of uncertainty in ECMWF’s ensemble forecasts of the Integrated Forecasting System (IFS) as well as the maintenance of the tangent linear (TL) and adjoint (AD) code for the IFS physical parametrisations used during the minimisation process of 4DVar data assimilation. The work on the uncertainty representation includes the current operational stochastically perturbed parameterisations (SPP) scheme, the use of…
singular vectors and the uptake of initial conditions from the ensemble data assimilation system. Both the uncertainty representation and TL/AD have an impact on the quality of the world-leading data assimilation and physical ensemble forecast system for numerical weather prediction at ECMWF. The successful candidate will also support developments of the Artificial Intelligence Forecasting System (AIFS) relevant for ensemble forecasting, providing advice on the representation of physical processes in data-driven ensemble forecasts, helping with the generation of training datasets, and potentially working hands-on with machine-learned ensemble models. The work requires both technical expertise to create stable and resilient model configurations and a good understanding of the underlying physical processes and mathematical algorithms of the IFS.
This position is based in the Model Uncertainty Team, responsible for the development of both the physical IFS ensemble and the machine-learned AIFS ensemble systems across forecast lead times. The work will be carried out in very close collaboration with the Physical Processes Team and the teams working on data assimilation for both the operational analysis to create initial conditions for operational weather prediction and for reanalysis products such as ERA6.
At ECMWF, you will find a passionate community, collectively aiming to build world-leading global Earth system models for numerical weather prediction. This effort supports ECMWF’s strategy of producing cutting‐edge science and world-leading weather predictions and monitoring of the Earth system.
Your team
The Model Uncertainty team, part of the Earth System Modelling Section of the Research Department, works on the quantification of forecast uncertainties due to the representation of initial uncertainties and the representation of model uncertainties. The team contributes to the design of the ensemble configuration and develops new approaches for uncertainty representation in all components of the Earth System and hence in collaboration with all teams in the section.
Your responsibilities
- Enhance representations of uncertainties (such as the SPP stochastic parametrisation scheme) for use in numerical weather predictions across forecast lead times (from days to seasons) and for km-scale model simulations and Digital Twins of the Earth system
- Maintain and update the tangent linear (TL) and adjoint (AD) model code for the physical parametrisation schemes of the IFS. This includes the maintenance of the existing approach for TL/AD generation and testing, but also the exploration of new methods including automatic differentiation and deep learning emulation
- Support developments of the AIFS ensemble system via insight into the representation of physical processes and the generation of datasets for training data-driven ensemble models
What we're looking for
- Excellent analytical and problem-solving skills with a proactive approach to improve models and tools. The work on both ensembles and TL/AD model code requires a diligent approach and a deep understanding of theory and detail
- Excellent interpersonal and communication skills
- Self-motivated and able to work with minimal supervision, but also dedicated and enthusiastic about teamwork with willingness to work in close collaboration
- Ability to maintain effective communication and documentation of scientific results
- Highly organised with the capacity to work on a diverse range of tasks to tight deadlines
Your profile - Education, experience, knowledge and skills
- Advanced university degree (EQ7 level or above) in a physical, mathematical or environmental science, or equivalent professional experience
- Experience in Earth system modelling including contributions to code and the use of large simulations on modern supercomputing environments
- Experience in stochastic parametrisation schemes and/or the generation of tangent linear and adjoint model code handling is desirable
- Expertise in atmospheric physical processes, numerical weather prediction and the methodology of operational weather prediction is desirable
- Candidates must be able to work effectively in English
About ECMWF
The European Centre for Medium-Range Weather Forecasts (ECMWF) is a world leader in Numerical Weather Predictions providing high-quality data for weather forecasts and environmental monitoring. As an intergovernmental organisation we collaborate internationally to serve our members and the wider community with global weather predictions, data and training activities that are critical to contribute to safe and thriving societies.
The success of our activities depends on the funding and partnerships of our 35 Member and Co-operating States who provide the support and direction of our work. Our talented staff together with the international scientific community, and our powerful supercomputing capabilities, are the core of a 24/7 research and operational centre with a focus on medium and long-range predictions. We also hold one of the largest meteorological data archives in the world.
Our mission: Deliver global numerical weather predictions focusing on the medium-range and monitoring of the Earth system to and with our Member States
Our vision: World-leading monitoring and predictions of the Earth System enabled by cutting-edge physical, computational and data science, resulting from a close collaboration between ECMWF and the members of the European Meteorological Infrastructure, will contribute to a safe and thriving society
In addition, ECMWF has established 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, as well as being a contributor to the Copernicus Emergency Management Service. 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 its headquarters in Reading, UK, a data centre in Bologna, Italy, and a large presence in Bonn, Germany as a central location for our EU-related activities. ECMWF is internationally recognised as the voice of expertise in numerical weather predictions for forecasts and climate science.
About Copernicus www.ecmwf.int/
About Copernicus https://www.copernicus.eu/en
About Destination Earth destine.ecmwf.int
Other information
Grade remuneration: The successful candidate will be recruited at either the A2 or A3 grade, depending on relevant experience, 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-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. To find out more about working with us and for full details of salary scales and allowances, please visit www.ecmwf.int/en/about/jobs/working-ecmwf.
Starting date:From 01 June 2026
Length of contract: The contract duration is expected to be four years. There may be the possibility of further contract extensions in the future, depending on requirements and funding availability.
Location: The position can be based either at ECMWF's headquarters in Reading, UK or at our office location in Bonn, Germany. The successful candidate is expected to relocate to the duty station, and relocation support is provided.
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 by videoconference (via MS Teams).
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.
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.