Job Description

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 Modelling section of the ECMWF Research Department. The Fellow will primarily collaborate with the Physical Processes team, as well as with scientists from the Machine Learning team. The work will also involve support from and collaboration with the Center for Earth System Observation and Computational Analysis, a joint initiative of the University of Bonn, the University of Cologne, and Forschungszentrum Jülich.

This is what we need you for:

Extreme events such as severe storms and flooding are frequently associated with intense convective activity, and yet forecasting convection remains inherently uncertain. Because convection interacts with its environment across a wide range of spatial and temporal scales and strongly affects the large-scale atmospheric circulation, it is a key source of uncertainty in medium-range weather prediction. Over recent years, AI-based weather forecasting has advanced rapidly. While large-scale forecast skill now often exceeds that of traditional physics-based models, AI/ML systems such as ECMWF’s AIFS lack physical representation as well as temporal and spatial resolution to represent convection. We therefore intend to integrate insights from both physical and AI-based weather prediction to create synergies that improve the physical realism of AI models while simultaneously informing the development of physical NWP models.

The responsibilities include the following key areas:

Compare and evaluate tropical flow in IFS and AIFS, with a focus on the 3D representation of deep convective systems
Design and conduct experiments in IFS and AIFS to understand causalities, e.g., by adding idealised perturbations to the models
Investigate and evaluate ways how the physical representation of convection in AIFS could be improved
Use insights from IFS-AIFS comparison to advance physical model development
Document and communicate scientific results, e.g., in peer-reviewed publications and at conferences

This position is based within the Predictability Section of the Research Department at ECMWF. The fellow is going to collaborate closely with researchers in the Section and across ECMWF to drive forward the development of subseasonal-to-seasonal (s2s) predictions with AIFS in the Anemoi framework. The work will be carried out in collaboration with the Center for Earth System Observation and Computational Analysis (CESOC) at the University of Cologne and Forschungszentrum Jülich.

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 www.ecmwf.int/ 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:

Strong foundational knowledge in machine learning and atmospheric science
Experience in handling large datasets with Python-based scientific workflows is desirable
Meteorology background with experience in physical modelling of convection is an advantage

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.