Jesper Dramsch

Scientist
Forecast, Machine Learning - Engineering

Summary:

Jesper Dramsch (they/them) is a Scientist for Machine Learning at the European Centre for Medium-Range Weather Forecasts, where they implement state-of-the-art machine learning solutions for numerical weather prediction. They are a core developer of AIFS (Artificial Intelligence Forecasting System), ECMWF's fully data-driven NWP model, focusing on graph neural networks for weather forecasting. Jesper has led critical technical innovations, including the refactoring of AIFS infrastructure, implementation of hydra-based configuration systems, and development of operational ai-models plugins for FourCastNet, GraphCast, and AIFS. They have established code quality standards through pre-commit hooks and unit testing frameworks, enabling the scaling of the AIFS team. Jesper Dramsch co-led the development of the data-driven weather forecasting ecosystem Anemoi, used by national weather services globally. Beyond technical contributions, Jesper co-organised ECMWF's first MOOC on Machine Learning in Weather & Climate, reaching thousands of participants globally, and continues to organise ML and AIFS training courses for member states. They serve as co-chair of the Working Group Modelling for the UN ITU Resolutions Global Initiative on AI for Natural Disaster Management. Jesper has successfully bridged the gap between research and operations, ensuring reproducibility and maintaining close collaboration with domain scientists and Member State institutions like DWD.

Jesper brings an interdisciplinary background spanning geophysics, physics, and machine learning to their work at ECMWF. Prior to joining ECMWF, they completed a PhD at the Technical University of Denmark, worked as a Machine Learning Engineer at GMV NSL, and served as a consultant educator in Python and ML. Their commitment to science communication extends beyond ECMWF through multiple channels: teaching on Skillshare, which has over 10,000 students, maintaining the "Late to the Party" newsletter with more than 1,111 subscribers, and achieving YouTube Partner status. As a Fellow of the Software Sustainability Institute, they champion reproducible research practices and maintain several open-source projects, including PythonDeadlin.es and ML.recipes. Jesper has contributed to major ML frameworks through documentation for TensorFlow, Scikit-Learn, and Pandas, and has been recognised as a Kaggle Top 81 contributor. Their previous work at ECMWF included hybrid machine learning approaches for post-processing, observational operators, and S2S forecasting, as well as, collaboration with Microsoft on the PoET project and contributions to the ITU Focus Group on AI for Natural Disaster Management.

 

Key Responsibilities

 

Learn More

Jesper Dramsch field work
Bringing together users, researchers and coders in ECMWF’s machine learning efforts  

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     Visit dramsch.net for more details.

    Professional interests:
     Machine Learning
    • Validation of machine learning models in real-world contexts
    • Machine learning in science and reproducibility
    • Machine learning for weather and climate predictions
    Research Software Engineering
    • Research software engineering and sustainable software
    • Code quality and reproducibility
    • Python ecosystem and open-source
    Modelling
    • System design
    • Testing implicit assumptions of modelling choices
    • Inversion problems
    Outreach, Education & Inclusion
    • Education and communication of cutting-edge research
    • Mental health and neurodivergence advocacy
    • Gender equality and intersectionality
    Career background:
    Education

    PhD, Technical University of Denmark, Denmark (awarded 2021). Thesis: "Machine Learning in Geoscience: Applications of Deep Neural Networks in 4D Seismic Data Analysis". Supervisor: Dr. Mikael Luethje. dramsch.net/phd

    MSc, University of Hamburg, Germany (awarded 2014). Thesis: "Seismic subsalt imaging with prestack data enhancement methods." Supervisor: Prof. Dr. Dirk Gajewski. osf.io/preprints/thesiscommons/aec7p_v1

    BSc, University of Hamburg, Germany (awarded 2011). Thesis: "Trace interpolation with partial crs-stacks." Supervisor: Prof. Dr. Dirk Gajewski. thesiscommons.org/mvxuh/

    Professional Experience 
    • 2021 - present: Scientist for Machine Learning, ECMWF
    • 2020 - 2021: Machine Learning Engineer, GMV NSL
    • 2019: Postdoc, Technical University of Denmark
    • 2018 - 2019: Visiting Scholar, Heriot-Watt University, Edinburgh


    For a full career background, check: LinkedIn

     Teaching Experience
    • 2025: Co-organised and held Anemoi webinar series
    • 2023: Guest lecturer ML for NWP, Brown University
    • 2023: Co-organiser and lecturer ML for Weather and Climate Prediction, ECMWF MOOC
    • 2022 - 2024: Lecturer and organiser for ML training courses, ECMWF

     

    For a comprehensive list, check dramsch.net/teaching

    Open-Source Contributions

    Maintainer

    Anemoi Core
    ML.recipes
    PythonDeadlin.es

    Documentation
    Tensorflow
    Scikit-Learn
    Pandas

    For a full overview of open-source contributions, check GitHub

     

    External recognitions
    Awards & Recognitions
    • 2025: EMS Technology Achievement Award 2025 (as contributor and part of Anemoi)
    • 2024: Co-chair WG Modelling of the Resolutions Global Initiative
    • 2022: Fellow of the Software Sustainability Institute for ML for Science
    • 2022: YouTube Partner
    • 2021: Contributing member to WG Data, ITU Focus Group for AI 4 Natural Disaster Management
    • 2019: Top 81 worldwide Kaggle Code

     

    Presentations & Media Appearances 

    Invited: Climademics Summer School at Robert-Koch-Intritute, Guest lecture at Brown U, Lecture Climate Research Centre Singapore, Presentation National University of Singapore, Guest Lecture at University of Hamburg, Keynote at EAGE Workshop on Seismic interpretation with AI, Session Chair Atmospheric Science Conference, Invited Talk NVBM Symposium, PyData Global Big Data Panel, Pydata Global Impact Panel; CfP: PyCon Germany, EuroScipy Talk, EuroScipy Tutorial, PyData Global Talks, PyData Global Workshop; Podcast: Data Scientist Show, Code for Thought, Software World, MidMeetPy, Undersampled Radio; AcademicSession Chair ECMWF ML Workshop, EAGE Presentations & Posters, SEG Presentation Other: ECMWF Training, MOOC, SSI Fellows Update, SSI Community Call

    For a full list, check: dramsch.net/speaker