Salary and Grade:
Grade A2 GBP 73,094 (Reading/UK) or EUR 89,168 (Bonn/Germany) NET annual basic salary + other benefits Deadline for applications:
26/01/2025 Department:
Forecasts and Services Location:
Reading, UK or Bonn, Germany Contract type:
STF-PL Contract Duration:
2.5 years up to July 2027, with possibility of extensions Join Our Team: Pioneering Hydrological Forecasting with Machine Learning
Are you a forward-thinking scientist eager to revolutionize hydrological forecasting? ECMWF invites you to join our dynamic team and help shape the future of global environmental predictions through innovative machine learning (ML) approaches. Your Impact
In this role, you will: Advance the state-of-the-art in global flood forecasting through the application of deep learning Design, implement, and evaluate models to predict discharge, water levels, and flood inundation maps Collaborate closely with the Hydrology Team and ML experts to develop innovative forecasting solutions Lead the integration of machine-learned hydrological components as part of a European foundation model Collaborate with the AIFS team and the ANEMOI project to adapt techniques from ECMWF’s global machine-learned weather model (AIFS) for hydrological forecasting Who You Are
A team player who thrives on collaboration and values shared success Enthusiastic for continuous learning, staying current with the latest trends, research, and developments in AI and ML Self-motivated, efficient, and eager to contribute to technical discussions Skilled at documenting and communicating scientific developments effectively Your Background
Advanced university degree (EQ7 level or above) in a physical, computing, mathematical, or environmental science, or equivalent professional experience. Expertise in at least one deep learning framework (PyTorch, Tensorflow, JAX) Experience in the general areas of machine learning and scientific computing Experience with developing and applying machine learning emulators, particularly utilizing architectures such as graph neural networks and transformers Experience with hydrological datasets from model outputs, satellites, or observations is an advantage Fluency in English (knowledge of French or German is a plus) Why This Role?
At ECMWF, you’ll work at the cutting edge of science and technology, surrounded by passionate professionals dedicated to making a real-world impact. This is your chance to contribute to innovative solutions for some of the most pressing challenges of our time, from climate resilience to disaster risk reduction. Apply today and take the next step in your scientific career while making a tangible difference on a global scale. About ECMWF
The European Centre for Medium-Range Weather Forecasts (ECMWF) is an intergovernmental organisation created in 1975 and is today supported by 35 Member and Co-operating States mostly in Europe. The Centre’s mission is to serve and support its Member and Co-operating States and the wider community by developing and providing world-leading global numerical weather prediction. Other information
Grade remuneration
The successful candidate will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. ECMWF also offers a generous benefits package, including a flexible teleworking policy. The position is assigned to the employment category STF-PL as defined in the ECMWF Staff Regulations. Starting date:
As soon as possible Interviews will take place via videoconference (MS Team). If you require any special accommodations in order to participate fully in our recruitment process, please contact us via email: jobs@ecmwf.int Who can apply
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. Applications are invited from nationals from ECMWF Member States and Cooperating States, as well as from all EU Member States. In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy. Applications from nationals from other countries may be considered in exceptional cases.
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Grade A2 GBP 73,094 (Reading/UK) or EUR 89,168 (Bonn/Germany) NET annual basic salary + other benefits Deadline for applications:
26/01/2025 Department:
Forecasts and Services Location:
Reading, UK or Bonn, Germany Contract type:
STF-PL Contract Duration:
2.5 years up to July 2027, with possibility of extensions Join Our Team: Pioneering Hydrological Forecasting with Machine Learning
Are you a forward-thinking scientist eager to revolutionize hydrological forecasting? ECMWF invites you to join our dynamic team and help shape the future of global environmental predictions through innovative machine learning (ML) approaches. Your Impact
In this role, you will: Advance the state-of-the-art in global flood forecasting through the application of deep learning Design, implement, and evaluate models to predict discharge, water levels, and flood inundation maps Collaborate closely with the Hydrology Team and ML experts to develop innovative forecasting solutions Lead the integration of machine-learned hydrological components as part of a European foundation model Collaborate with the AIFS team and the ANEMOI project to adapt techniques from ECMWF’s global machine-learned weather model (AIFS) for hydrological forecasting Who You Are
A team player who thrives on collaboration and values shared success Enthusiastic for continuous learning, staying current with the latest trends, research, and developments in AI and ML Self-motivated, efficient, and eager to contribute to technical discussions Skilled at documenting and communicating scientific developments effectively Your Background
Advanced university degree (EQ7 level or above) in a physical, computing, mathematical, or environmental science, or equivalent professional experience. Expertise in at least one deep learning framework (PyTorch, Tensorflow, JAX) Experience in the general areas of machine learning and scientific computing Experience with developing and applying machine learning emulators, particularly utilizing architectures such as graph neural networks and transformers Experience with hydrological datasets from model outputs, satellites, or observations is an advantage Fluency in English (knowledge of French or German is a plus) Why This Role?
At ECMWF, you’ll work at the cutting edge of science and technology, surrounded by passionate professionals dedicated to making a real-world impact. This is your chance to contribute to innovative solutions for some of the most pressing challenges of our time, from climate resilience to disaster risk reduction. Apply today and take the next step in your scientific career while making a tangible difference on a global scale. About ECMWF
The European Centre for Medium-Range Weather Forecasts (ECMWF) is an intergovernmental organisation created in 1975 and is today supported by 35 Member and Co-operating States mostly in Europe. The Centre’s mission is to serve and support its Member and Co-operating States and the wider community by developing and providing world-leading global numerical weather prediction. Other information
Grade remuneration
The successful candidate will be recruited at the A2 grade, according to the scales of the Co-ordinated Organisations. ECMWF also offers a generous benefits package, including a flexible teleworking policy. The position is assigned to the employment category STF-PL as defined in the ECMWF Staff Regulations. Starting date:
As soon as possible Interviews will take place via videoconference (MS Team). If you require any special accommodations in order to participate fully in our recruitment process, please contact us via email: jobs@ecmwf.int Who can apply
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. Applications are invited from nationals from ECMWF Member States and Cooperating States, as well as from all EU Member States. In these exceptional times, we also welcome applications from Ukrainian nationals for this vacancy. Applications from nationals from other countries may be considered in exceptional cases.
#J-18808-Ljbffr
