AI-Driven Weather Forecast Scientist (Satellite ML), Locarno

Project background Our research fellowship supported by EUMETSAT aims to integrate geostationary satellite observations into a next‑generation regional machine‑learning weather prediction system. The project builds on an existing graph‑based regional forecasting model at MeteoSwiss and is embedded in the Anemoi framework initiated by ECMWF. The objective is to develop and evaluate novel multi‑encoder‑decoder architectures that ingest various satellite data streams (radiances, cloud products, lightning observations, hyperspectral soundings) for high‑frequency forecasting from short‑range to up to 10‑day lead times.

Job description We are looking for a Scientific Programmer / Software Developer to join our interdisciplinary team.

Responsibilities

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

Qualifications

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 (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 centres, 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 organisation

The opportunity to combine scientific impact, societal relevance, and modern software engineering

Equal opportunity statement EUMETSAT and ETH Zürich are committed to providing an equal opportunities work environment for men and women. Only nationals of EUMETSAT Member States may apply.

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