Several locations: Shinfield Park Reading, United Kingdom; Robert-Schuman-Platz 3 Bonn, Germany
Job reference: VN26-33
Salary and Grade: Grade A3, Reading, UK: £94,251 or Bonn, Germany: €113,224 NET annual basic salary + other benefits
Deadline for applications: 07/05/2026
Department: Research
Location: Reading, UK or Bonn, Germany
Contract type: STF-C
Publication date: 10/04/2026
Contract Duration: Four years, with the possibility of future contracts
Your role
We are seeking a Team Leader for Sub-Seasonal Forecasting, to lead the ongoing scientific development of sub-seasonal forecasting at ECMWF within the Research Department.
ECMWF delivers operational forecasts to its stakeholders on a range of timescales from the medium-range up to multiple seasons and beyond. Operational delivery includes world-leading numerical sub-seasonal forecasts, produced using a configuration of ECMWFs Earth System model together with carefully prepared ensemble initial conditions.
The role involves leading a small team of scientists working to improve understanding of earth-system sub-seasonal forecast performance, processes and predictability, and to drive improvements in ECMWF forecast performance. The team works in close collaboration with teams across ECMWF, including those involved in developing forecast system components (atmosphere, ocean, land), teams developing data assimilation and ensemble initialization, those working on forecasting at other timescales, technical development teams, and those focussed on user products.
About the Sub-Seasonal Forecasting Team
The sub-seasonal forecasting team is part of the Earth System Predictability Section in ECMWF’s Research Department, which also includes a Seasonal Forecasting Team. Both teams work to advance ECMWF’s forecast capabilities at their respective timescales. Given the strong scientific and methodological overlaps between forecasting at these timescales, the two teams collaborate closely.
The sub-seasonal team presently consists of 10 scientists, the majority supported by external funding (a mixture of DestinE and Horizon projects). The team develops and provides scientific support to operational sub-seasonal forecast systems at ECMWF.
At present ECMWF operates a physics-based sub-seasonal ensemble forecast system based on the Integrated Forecast System (IFS). This system runs a 101-member ensemble every day out to 45 days, together with an associated set of reforecasts covering the previous 20 years. The model version and configuration used are identical to the 9-km medium-range forecast, except for the atmospheric resolution, which is 35-km. The sub-seasonal forecast system is updated in parallel with the medium-range configuration, and any changes to the IFS (model, 4d-var, ensemble perturbations) are tested at both timescales before operational implementation.
A machine-learning based sub-seasonal forecasting system is planned for introduction later in 2026, to run alongside the physics-based forecast. Several members of the team are actively developing sub-seasonal Machine learning approaches for sub-seasonal forecasts, and it is envisaged that both physics-based and ML-based techniques will contribute to operational forecasting in the coming years.
Your responsibilities
- Lead and manage a talented team of scientists, driving the ongoing development and improvement of ECMWF’s sub-seasonal forecasting systems, including both physics-based and machine-learning-based models.
- Contribute scientific expertise to the development and assessment of ECMWF forecasts at sub-seasonal and across timescales.
- Ensure the scientific integrity of ECMWF’s sub-seasonal forecasting systems, and that forecast system developments meet the needs of users and applications.
- Represent ECMWF’s sub-seasonal forecasting both internally and in international scientific and operational communities
- Seek and secure external funding to support targeted research needs
What we are looking for
Education
- An excellent university degree and a PhD (EQF Level 8) in climate science, mathematics, physics or a related field
- A substantial number of years' experience in relevant scientific research
- An appropriate record of scientific publications
Experience, Knowledge and Skills
- Scientific excellence with a strong track record in research relevant to atmospheric and Earth system sciences
- Experience in operational forecasting research, including understanding how scientific advances translate into forecast improvements
- Domain expertise in sub-seasonal prediction, including both physics-based and emerging machine-learning approaches
- Proven team leadership skills, with the ability to manage, motivate, and develop a group of scientists
- Expert knowledge of atmospheric dynamics and physical climate processes relevant to sub-seasonal timescales
- Extensive experience in designing, running, and evaluating forecast experiments, including the application of appropriate statistical methods
- Proven capability to operate and work with physics-based forecasting models in a research or operational context
- Familiarity with machine-learning-based weather forecasting, with experience using ML forecasting systems considered an advantage
- Strong programming and scripting skills, enabling efficient development and analysis of forecasting experiments
- Demonstrated project management experience, including planning, coordinating, and delivering research activities
- Excellent communication skills, including the ability to convey complex scientific concepts to diverse audiences
We encourage you to apply even if you feel you don't precisely meet all these criteria.
Candidates must be able to work effectively in English and interviews will be conducted in English. A good knowledge of one of the Centre’s other working languages (French or German) is an advantage.
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.
Other information
Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations. In addition to basic salary, ECMWF also offers an attractive benefits package.
Starting date: asap
Location: Reading, UK or Bonn, Germany (Candidates are expected to relocate to the duty station)
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).
Who can apply
Applications are invited from nationals from ECMWF Member States and Co-operating States.
