Data Scientist for Weather Forecasting using Machine Learning

Data Scientist for Weather Forecasting using Machine Learning This project arises from a collaboration between the Swiss Data Science Center (SDSC), MeteoSwiss, and EUMETNET . The SDSC has been a National Research Infrastructure since 2025, evolving from a strategic focus area of the ETH domain, with EPFL and ETH Zurich as founding partners. Its mandate is to support academic groups and research, hospitals, industry, and the public sector at large, including cantonal and federal administrations. The center accompanies and supports their entire data science journey, from the collection and management of data to machine learning, AI, and industrialization. As part of the mission of the SDSC, we are also tackling problems arising in several domains: Climate, Weather, and Environmental Sciences is a vertical within SDSC of which we strongly contribute. The Center comprises a multi-disciplinary team of data and computer scientists and experts in several domains, with offices in Zürich, Lausanne, and Villigen. MeteoSwiss, the Federal office for Meteorology and Climatology, leads and coordinates a larger effort on ML-based weather forecasting together with its European partners, including other national weather services and ECMWF. In its role, MeteoSwiss will provide domain science support, access to data and know-how, and will support the onboarding onto ongoing efforts and existing codebases. MeteoSwiss is also responsible for prototyping and operationalization of the developments done in the context of this project. As such, it defines requirements and provides feedback on the outcomes. The advertised position is funded by EUMETNET (the European Meteorological Network) programme on Artificial Intelligence and Machine Learning for Weather, Climate and Environmental Applications (E-AI). EUMETNET is a network of the 33 European National Meteorological Service, which exists to provide a framework to organise co-operative programmes between the members in fields of meteorology, data processing and forecasting products. Project background We are looking to hire a data scientist with expertise in machine and deep learning for the development of a seamless neural weather forecasting system. This project is framed as a collaboration involving the Swiss Data Science Center, MeteoSwiss, and EUMETNET, and it is funded for 2 years. The goal is to develop and implement a system able to perform forecasting at various temporal scales up to 10 days, using recent developments in deep learning and machine learning. We expect the candidate not only to contribute to methodological advances, but also to develop and contribute to existing codebases and open-source initiatives in the domain, with good quality, reproducible, and robust code in Python. The candidate will make use of and contribute to Anemoi , a toolbox for the use and implementation of AI tools for weather and climate applications, developed by the European Center for Medium-range Weather Forecast (ECMWF). Another important project goal is to assess the integration of such systems into operational forecasting services, complementing numerical and statistical models currently used at MeteoSwiss but ideally at other European Weather Services. The candidate is also expected to participate in frequent meetings and updates with relevant stakeholders, such as MeteoSwiss and other EUMETNET members and advisory committees. The position is located at the Swiss Data Science Center, allowing the candidate to interact with a wide variety of experts in the machine learning and data science domains. Our diverse and interdisciplinary environment offers the candidate the opportunity to engage in all types of research discussions, and advance in the methods and the domains fields alike. Job description Main duties and responsibilities include: You will focus on collecting data and streamlining data products, in collaboration with MeteoSwiss and relevant European data providers, and coding a dedicated anemoi-dataset parsing routine for efficient training and inference purposes You will review and implement baselines and state-of-the-art methods relevant to the task, and critically assess them You will develop, in collaboration with MeteoSwiss scientists and engineers, the weather forecasting models, the core of this project You will use and implement assessment and verification tools, interpret them, and communicate relevant results to stakeholders You will complement and contribute to the ANEMOI library, where relevant (baselines, assessment, own methods, own functionalities) You will engage with the re