Climate Data and AI/Machine Learning Scientist (ArcX Climate Change Resilience)

ECMWF is seeking an enthusiastic Machine Learning Scientist - ArcX Climate Change to help deliver a step change in the use of machine learning methods for sub-seasonal to seasonal prediction over Africa. Under the EU Global Gateway programme for Africa Regional Centres of Excellence (ArcX), you will bring in the scientific and technical expertise into SEWA and ArcX-Climate Change Resilience on the use of AI/ML in sub-seasonal to seasonal forecasts time scales, in coordination with scientists and operators at African regional climate Centres (RCCs). In addition, your role will be to bridge research and innovation with specific users’ needs in an African context.

Working closely with ECMWF scientific and technical experts, as well as European and African partners, you will coordinate and contribute to research adapting Anemoi and ECMWF’s Artificial Intelligence/Integrated Forecasting System (AIFS) to produce the most skilful and reliable forecasts for weeks 3 and 4 over Africa. You will work in the Sub-seasonal Team in the Predictability Section as well as other machine learning experts across ECMWF. This will enable development of the best possible machine learning models for Africa that exploit the full potential of machine learning and high-performance computing while still being tested and evaluated by domain scientists who check for physical consistency and limits in predictability. You will also coordinate and deliver related activities, including ECMWF training focused on AI/ML in close collaboration with technical and scientific colleagues.

Your Responsibilities:

• Lead research for improved AI/ML sub-seasonal predictions over Africa
• Coordinate and contribute to the development of improved machine learning based predictions over Africa two to four weeks ahead
• Act as an Anemoi expert for the project goals, including delivering Anemoi education and contributing to Anemoi developments
• Contribute to the evaluation of the AI sub-seasonal forecasts over Africa in coordination with scientists and operators at African regional climate Centres (RCCs)
• Steer, as a Technical Officer, a grant on the implementation of the AIFS within an African regional context, through e.g. downscaling and steer contractors working on two demonstration cases in which the use of ML in sub-seasonal to seasonal forecasts over Africa is investigated.
• Support interactions with JRC and WMO as key stakeholders in ArcX
• Provide support to innovation competitions under ArcX, including the AI Weather Quest for Africa.
• Provide inputs to peer-to-peer training activities, incl. drafting training material and attending training sessions in Africa as a topic specialist, in coordination with other topic specialists
• Support the drafting and publication of scientific articles on results obtained in ArcX
• Attend meetings of the different WMO Regional Climate Observation Forums (RCOFs) as topic specialist

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.

What We Are Looking For:

• Enthusiastic and excellent engagement and networking skills
• Excellent written and verbal communication skills with the ability to communicate to different and multi-cultural audiences
• Good team player with initiative and ability to work collaboratively in an interdisciplinary and multi-site environment with domain scientists, machine learning scientists and computing scientists, but also ability to work independently
• Excellent analytical and problem-solving skills with a proactive and constructive approach
• Flexibility, with the ability to adapt to changing priorities
• Highly organised with the capacity to work on a diverse range of tasks to tight deadlines

Your profile:

• Advanced university degree (EQ7 level or above) in Earth System Science, Physics, Applied Mathematics, Computer Science or a related discipline, or equivalent professional experience
• Experience using Python and interaction with large geophysical datasets
• Experience in the use of machine learning, and knowledge of deep learning architectures, preferably in the field of weather and climate
• Experience with the development and diagnostics of general circulation models is desirable
• Experience with technical management of scientific, multi-partner projects is an advantage
• Knowledge of dynamical meteorology and predictability across time scales is desirable
• Some experience with communicating scientific results to a general audience and the writing of scientific reports would be beneficial
• In the context of working with partners from sub-Saharan Africa, a working knowledge of French or Portuguese is an advantage.
• Candidates must be able to work effectively in English; knowledge of one of the Centre’s other working languages (French or German) is an advantage.

If you feel that you have the relevant profile and motivation to join us but don't meet precisely all of the skills above, we still encourage you to apply!

Other Information:

• Grade remuneration: The successful candidates will be recruited according to the scales of the Co-ordinated Organisations.
• Starting date: as soon as possible.
• Candidates are expected to relocate to the duty station, either to Bonn, Germany, or to Reading, UK.
• Interviews by videoconference (MS Team) are expected to take place within a month of the vacancy closing date.

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.