Rejoignez l'équipe de météorologie d'ETH Zürich, innovante et dynamique. Profitez d'une opportunité unique de participer à des projets de prévision météo.
Tâches
Développer des architectures de modèles d'apprentissage automatique.
Contribuer à un cadre MLWP au sein de l'écosystème Anemoi.
Évaluer l'impact des données satellite sur la prévision.
Compétences
Doctorat en informatique ou sciences naturelles requis.
Compétences en programmation Python indispensables.
Expérience en apprentissage automatique souhaitée.
Machine Learning Scientist (AI-based Weather Forecasting)
Recent advances in AI-based weather prediction have demonstrated remarkable skill and computational efficiency. However, most current machine-learning weather prediction (MLWP) systems rely primarily on NWP analyses for initialization and only partially exploit the wealth of available satellite observations. With the advent of the Meteosat Third Generation (MTG), new high-frequency and high-resolution measurements of clouds, moisture, temperature, and lightning activity provide unprecedented opportunities for substantially improving regional forecasts.
Project background
Within the framework of a Research Fellowship supported by EUMETSAT, we are advancing the integration of geostationary satellite observations into a next-generation regional MLWP system. The project builds on an existing graph-based, stretched-grid regional forecasting model developed at MeteoSwiss and is embedded in the Anemoi framework initiated by ECMWF. The objective is to develop and evaluate novel multi-encoder-decoder architectures capable of ingesting various satellite data streams (e.g. radiances, cloud products, lightning observations, hyperspectral soundings) and integrating them into high-frequency forecasting cycles with lead times from short-range to up to 10 days ahead. The work will be carried out in close collaboration with national and international partners and contributes directly to the future operational exploitation of MTG data.
Job description
We are looking for a Scientific Programmer / Software Developer to join our motivated and interdisciplinary team.
In this role, you will:
Develop and implement machine-learning model architectures enabling the direct ingestion of next generation satellite data (e.g. MTG FCI, LI, IRS) into state-of-the-art regional forecasting models in Anemoi
Contribute to the evolution of a multi-encoder-decoder MLWP framework within the Anemoi ecosystem
Train, fine-tune, and evaluate models using large-scale meteorological and satellite datasets
Quantify the impact of satellite data on forecast skill across variables and lead times
Collaborate closely with scientists, ML researchers, and operational forecasting teams to ensure that forecast outputs meet the needs of diverse users
Disseminate results through scientific publications, conference presentations, and exchanges with EUMETSAT and partner institutions
This is a fixed-term contract of 1 year, with the possibility of extension for up to an additional 2 years. The main workplace is located at MeteoSwiss Locarno-Monti with regular visits to Zürich. The amount of remuneration will be in accordance with the salary system of ETH Zürich.
Profile
We welcome applications from candidates with diverse backgrounds who meet most (not necessarily all) of the following criteria:
PhD in computer science, data science, natural sciences (e.g. physics, meteorology) or a related field. Candidates with an MSc and proven professional experience may also be considered
Experience working with satellite data (e.g. geostationary observations, radiances, retrieval products)
Strong programming skills in Python
Experience in machine learning, ideally including deep learning architectures such as graph neural networks, transformers, or spatio-temporal models
Experience with high-performance or distributed computing environments
Good understanding of meteorological processes and numerical weather prediction
Interest in DevOps practices and sustainable software engineering
Ability to work independently on research questions while contributing to a collaborative team environment
Motivation to work in a diverse, interdisciplinary, and international environment
Good communication skills (oral and written) in English and one of the Swiss national languages
We offer
Direct involvement in shaping next-generation AI-based weather forecasting systems
A unique opportunity to contribute to the operational exploitation of Meteosat Third Generation data
Direct involvement in bringing cutting-edge ML research into operational use
Close collaboration with European partners, including EUMETSAT, European national weather centers, ECMWF and the wider Anemoi community
Use of modern scientific and ML software stacks, including Python, PyTorch, Xarray, and container technologies on high-performance computing infrastructure
A supportive, motivated, and interdisciplinary team within a mission-driven public service organization
The opportunity to combine scientific impact, societal relevance, and modern software engineering
EUMETSAT and ETH Zürich are committed to providing an equal opportunities work environment for men and women. Please note that only nationals of EUMETSAT Member States may apply.
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