Climate, Energy and Machine Learning Systems

Who we are: We’re the CEALS group interested in developing and applying machine learning algorithms to enable decision makers to create a more sustainable future. The group just moved to the University of Augsburg (updates following soon).

Team

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Nicole Ludwig

Professor

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Claire Olivic Dongmo

PhD Student

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Florian Ebmeier

PhD Student

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Jannik Thümmel

PhD Student

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Kibidi Neocosmos

PhD Student

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Kornelius Raeth

PhD Student

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Luca Schmidt

PhD Student

Research Areas

Latest News

  • 30.04.2025: Join us for our workshop on time series forecasting and performativity.
  • 01.04.2025: Welcome Claire! Claire joins our group as an AIMS research fellow.
  • 15.03.2024: Kibidi won an Honorable Mention Award for his poster at the Machine Learning Summer School in Okinawa. Congrats Kibidi!
  • 16.11.2023: Our IN-ML-OUT project can now be seen at the Stadtmuseum Tübingen. For more details, follow the link
  • 10.11.2023: Nina's and Nicole's paper "Mind the (spectral) gap: How the temporal resolution of wind speed data affects multi-decadal wind power forecasts" got accepted! The paper is a result of a collaboration with Rachel White from UBC.

Teaching

Machine Learning for Renewable Energy Systems

We’re offering the Seminar on Machine Learning for Renewable Energy Systems in the Summer Term 2025.

Last updated on Apr 14, 2026
Bachelor's and Master's Thesis/ Research Projects

We do currently not accept any new student (thesis) projects.

Last updated on Apr 14, 2026
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Projects

IN-ML-OUT
Science Communication Project: Climate Change, Machine Learning and Wind Energy Forecasting
IN-ML-OUT
FEAT
BMBF funded research project: Machine Learning in Complex Systems under Uncertainty
FEAT

Recent Publications

Luca Schmidt, Nicole Ludwig (2024). Benchmarking Deep Learning Based Downscaling of Wind Speed.

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Kornelius Raeth, Nicole Ludwig (2024). Comparing Conditional Calibration of Aleatoric Uncertainty Estimators.

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Nina Effenberger, Marvin Pförtner, Philipp Hennig, Nicole Ludwig (2024). Probabilistic Wind Speed Downscaling for Future Wind Power Assessment.

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Florian Ebmeier, Nicole Ludwig, Jannik Thümmel, Georg Martius, Volker H. Franz (2024). Fault Detection in Solar Thermal Systems Using Probabilistic Reconstructions.

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Nina Effenberger, Nicole Ludwig, Rachel H. White (2023). Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts.

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Where to find us

  • Maria-von-Linden Str 6
    72076 Tübingen