The theme is connecting the research community at Chalmers with the potential and interest in applying AI to their scientific data analysis activities. The key is to lower the entry barrier for more researchers to use AI technologies for analysing their data.
On the applied side, AI-enhanced analysis of scientific data has very broad applicability and is becoming an essential technology for many research and engineering activities. On the more fundamental side, the analysis of scientific data provides a fertile ground to develop new AI tools, algorithms, and frameworks, several of which have recently been developed by researchers in the Chalmers research community (e.g., new algorithms for particle localization, tracking, and characterization).
However, there are several domains of research where the use of AI remains largely in its infancy. One of the root causes lies in an often too high threshold for scientists outside the machine-learning community to apply such concepts and tools.
The CHAIR theme aims to bring together groups in basic and applied sciences who can benefit from the applications of AI to their field. This will be achieved by establishing bridges between them and the existing community at Chalmers that already exploits machine learning in their research. The ultimate goal is to lower the barrier for collaboration and to facilitate knowledge sharing between them.
Giovanni Volpe, University of Gothenburg
- Researcher, Applied Chemistry, Chemistry and Chemical Engineering
- Senior Research Engineer, Onsala Space Observatory, Space, Earth and Environment
- Senior Researcher, Astronomy and Plasma Physics, Space, Earth and Environment
- Doctoral Student, Chemical Physics, Physics
- Deputy Head Of Department, Physics
- Researcher, Institution of physics at Gothenburg University
- Doctor, Institution of physics at Gothenburg University