Climate, Energy and Machine Learning Systems

Who we are: We’re the CEALS group within the Tübingen AI Center and the Cluster of Excellence – Machine Learning for Science at the University of Tübingen. We’re interested in developing and applying machine learning algorithms to enable decision makers to create a more sustainable future.

Team

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Nicole Ludwig

Jun. Professor

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Felix Strnad

PostDoc

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Stefano Gallo

PostDoc

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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

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Claire Olivic DONGMO

Research fellow

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 Jun 6, 2025
Bachelor's and Master's Thesis/ Research Projects

If you want to write your thesis or research project within the group, please write an e-mail to Thesis/ Projects MLSES with the following information

  • current transcript of records
  • topics you are interested in
  • your ideal starting date

Any E-Mails not going to the specified address or not containing the above information will not be responded to.

We currently have some limited capacity for master thesis projects during the winter term.

Last updated on Jun 6, 2025
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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