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
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.
This is what we need you for:
We are looking for a Fellow with a strong interest in shaping the future of s2s predictions using AI and machine-learning approaches. Forecasting these longer time scales with AI models is still a major scientific challenge. The Fellow will develop, implement and test innovative AI-driven approaches for s2s forecasting and thereby help drive progress in this rapidly evolving field.
An important aspect of the project will be the integration of Earth System components such as the ocean and land surface into the model, combined with an assessment of how this can contribute to increased s2s predictive skill in the atmosphere. A second important question to tackle is how to deal with limitations in the training data: shortness of the observational record as well as distribution shifts in the training data due to climate change have been found to be particularly challenging when training AI models for the s2s time range.
The responsibilities include the following key areas:
Propose, implement and test innovative AI model designs, training protocols and training data to advance s2s predictionsInvestigate the suitability of the AI s2s prediction systems to provide real-time forecasts that outperform existing benchmarks, for the benefit of users in Europe and beyondAssess physical realism, emergent natural variability, and generalizability under climate change of the AI s2s prediction systems developed, and investigate how they can be used as a research tool to understand weather and climate dynamics
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 meteorologyConfident 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 scienceBasic understanding of weather and climate predictability and predictionsExperience with Python-based scientific workflows and large geophysical datasets
Experience, Knowledge and Skills
Some knowledge of the German language, both spoken and written (Level A2 CEFR), is an advantageExcellent communication and information skills to create a sustainably positive and trusting atmosphere in interactions with peopleAbility to work constructively and collegiallyIndependence and initiative in solving problems appropriately within one’s own field of activityAbility to think and make judgments, to weigh up various familiar factors and form an appropriate judgmentEmpathy, in order to recognize the different needs of those involved in working in groups and to be able to take these needs into account appropriatelyPlanning and organizational skills with the ability to distinguish between urgent and important tasks over a short period of timeIntercultural competence as a prerequisite for successful collaboration in international teams and organizationsExperience 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: http://www.bav.bund.de/Einstieg-EBV
Further information is available in English here: https://www.bav.bund.de/SharedDocs/StellenangeboteEBV/20260571_0002.html?nn=210554
and in German here: https://www.bav.bund.de/SharedDocs/StellenangeboteEBV/20260572_0002.html?nn=210554
For more general information about the fellowship programme please check the DWD programme website:
https://www.dwd.de/DE/derdwd/arbeitgeber/einsteigen/fellowship/fellowship_en.html