MLCS aims to understand the interactions between different components of the climate system based on present-day and paleoclimatic data sets used along with the output of climate models. Climatic phenomena of interest include the El Niño Southern Oscillation (ENSO), the Global Monsoon (GM), and the Inter-Tropical Convergence Zone (ITCZ). The changes in the state of these climatic systems are consequential to people all around the planet, and they inform socio-economic decisions at all levels of societal organization. The ideas we use to infer hidden structure in climate data fall broadly under the category of Machine Learning (ML) approaches. ML concepts are inherently suited to the task as they are designed to identify, classify, and predict complex patterns.
If you are in Tübingen, then simply come by and say hello. We are located on the 4th floor of the AI Research Building at Maria-von-Linden-Str. 6.
- Bedartha Goswami (Group Leader)
- Elena Sizana (Administrative Assistant)
- Felix Strnad (Doctoral student)
- Jakob Schlör (Doctoral Student)
- Moritz Haas(Doctoral Student)
- Jannik Thümmel(Doctoral Student)
- Julia Hellmig(Masters Student)
- Ranganatha B R(Masters
- Benedict Roeder(Masters Student)
- Merle Kammer(Masters Student) Student)
- Davide Lussu(Bachelors Student)
- Jakob Unterholzner(Bachelors Student)