Early Career Fellow - Quantifying Observation Impact Across Physical and AI-Based Forecasting Systems

The DWD awards fellowships to early career scientists to work on a research project during a guest stay at ECMWF. Fellows will be based at the ECMWF offices in Bonn, Germany for a duration of two years. And extension for a 3rd year is possible in case you are undertaking a PhD in the context of the the fellowship.

The Early Career Fellow will work with scientists from different teams within ECMWF. The work will also include support by and collaboration with the Centre for Earth System Observation and Computational Analysis (University of Bonn, University of Cologne and Forschungszentrum Julich).

In addition to the research activities at ECMWF, an important component of the STEP UP! Fellowship program is a training program offered by DWD to support the Early Career Fellows’ professional and career development during their stay with ECMWF.

Your role

The position is in the Earth System Assimilation Section of the ECMWF Research Department. The Fellow will collaborate with scientists across ECMWF that develop classical, hybrid, and machine learning-based forecasting systems.

This is what we need you for:

The global observing system is fundamental to forecast skill, yet the impact of observations on the forecast error has almost exclusively been assessed within physics-based forecasting frameworks. The purpose of this position is to investigate and quantify the impact of observations on forecast error across physics-based, hybrid, and AI-driven forecasting systems, and to translate this understanding into guidance for future observing system design.

The responsibilities include the following key areas:

• Designing and conducting experiments to compare observation impact across the physically based IFS, hybrid AIFS, and fully data-driven systems
• Establishing a unified, cross-system understanding of how different observation types contribute to forecast error reduction
• Identifying strategically critical observations by determining which data sources are consistently impactful across systems, where sensitivities diverge, and where observational gaps may exist
• Developing diagnostic tools that enable attribution of forecast errors within ML and hybrid models
• Applying and advancing methodologies to assess the impact of observations on ML and hybrid models. Methodologies include data denial and sensitivity-based methods (e.g., FSOI)
• Communicating and documenting scientific results in technical reports, peer-reviewed publications, conferences, and collaborative meetings with Member States and partners

This role will contribute to a better understanding of how observations reduce forecast errors across different forecasting systems. Gained knowledge will help to inform future observing system design and potentially create a substantial downstream impact. 

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. We appreciate the need for flexibility in the way our staff work. We adopted a hybrid work model that is widely used by staff across ECMWF - allowing everyone to work in the office working as well as remotely up to 10 days/month, including away from the duty station.
 
ECMWF has also developed a strong partnership with the European Union and has been entrusted with the implementation and operation of the Climate Change and Atmosphere Monitoring Services of the EU Copernicus Programme. We also contribute 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.
See for more info about what we do.

What we're looking for

Required Qualifications:

• Successfully completed scientific or technical university degree (Bachelor, Master, Diploma), preferably in physics, mathematics, computer science, machine learning, environmental sciences, hydrology, oceanography or meteorology
• Confident knowledge of the English language, both spoken and written (at least level B2 CEFR)

Desirable competencies and skills:

• Experience in evaluating models with earth observations, statistical analysis and handling large datasets is desirable
• Expertise in numerical modelling, data assimilation, and machine learning is an advantage
• Foundational knowledge of Earth Observations, machine learning, and data analytics in Python is desirable

Experience, Knowledge and Skills

• Some knowledge of the German language, both spoken and written (Level A2 CEFR), is an advantage
• Excellent communication and information skills to create a sustainably positive and trusting atmosphere in interactions with people
• Ability to work constructively and collegially
• Independence and initiative in solving problems appropriately within one's own field of activity
• Ability to think and make judgments, to weigh up various familiar factors and form an appropriate judgment
• Empathy, in order to recognize the different needs of those involved in working in groups and to be able to take these needs into account appropriately
• Planning and organizational skills with the ability to distinguish between urgent and important tasks over a short period of time
• Intercultural competence as a prerequisite for successful collaboration in international teams and organizations
• Experience in cooperation with international organizations is an advantage

What we offer

The financial support awarded through the Fellowship amounts to EUR 2,900 per month for a period of 2 years, plus the individual travel expenses and publication costs as set out in the funding principles. The Fellowship grant also includes the participation in one (international) science conference per year, a specialist training and one day of job shadowing at the DWD.

The assignment will start in January 2027.

Who can apply

We value diversity and welcome all applications - regardless of age, gender or gender identity, nationality, ethnicity and culture, religion/belief, disability or sexual orientation.

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 (as of July 2022): 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.

There is no legal entitlement to the fellowship.

The basis for the fellowship awarding are the funding principles for the STEP UP! Fellowship programs, which are also part of the fellowship contract.

How to apply

Applications are managed outside ECMWF by DWD through the the German EBV:

Further information is available in English here:

and in German here:

For more general information about the fellowship programme please check the DWD programme website: