Understanding and shaping our environment is a fundamental need of humanity. As technology advances, our environment is being quantified on an ever-increasing scale, generating large amounts of diverse data such as weather and climate data, remote sensing and various sensor data but also text data. Generating knowledge and know-how from this wealth of data requires proven expertise in data science, machine learning and the modelling of complex systems, united under the framework of applied AI research. We have the theoretical knowledge and applied skills to bring the great progress in AI research to the world of environmental data.

Together with our partners we strive to solve problems in the domains of:

• Applied AI and complex system modelling in natural systems (agronomy, hydrology, environmental and bio-chemistry data)
• NLP for sustainability and digital environment
• Deep learning theory and applications

Our research group includes teaching engagements at BSc and MSc level as well as in continuing education. Here are the master theses we are currently supervising:

• Crop-yield models – a data-driven approach, with IUNR ZHAW (A. Rutzer)
• Flood modelling with neural cellular automata, with EAWAG/ETH (E. Gericke)
• Deep-learning assisted table-top NMR analysis, with EMPA/ETH (K. Koch)
• Scrutinizing sustainable economy with NLP methods (L. Sanfilippo)
• Artificial intelligence and NMR spectroscopy, with Bruker Biospins AG (C. Montagne)
• Analysis of climate change impacts using machine learning and GIS data, with IUNR ZHAW (H. Benkert)

• Helene Benkert, MSc Applied Computational Life Sciences
• Eric Lukas Gericke, MSc Applied Computational Life Sciences
• Kilian Koch, MSc Applied Computational Life Sciences
• Cédric Montagne, MSc Applied Computational Life Sciences
• Luca Sanfilippo, MSc Applied Computational Life Sciences