Cluster of Excellence – Machine Learning for Science

Research in the Cluster

Research Areas

Illustration of the research areas

Our mission is to enable machine learning algorithms to take a central role in all aspects of scientific discovery and to understand how such a transformation will impact the scientific approach as a whole. In particular, we aim  to drive new developments in machine learning, by identifying and solving overarching problems that are common to many scientific disciplines; to advance scientific application domains by creating a sustainable, transforming impact on science through machine learning; and to investigate long-range implications of the envisioned transformation of science through machine learning by studying possible consequences on the general scientific approach using methods from philosophy of science and research ethics. 

We identified four Research Areas in which progress is urgently needed to advance the use of machine learning in science and fully realize its potential to open new perspectives for science: 

Scientific application domains

In order to study the challenges, problems and opportunities for machine learning in science, we have chosen scientific application domains in three broad areas of science that will be considered during the first funding period of the cluster:

  • Life sciences: medicine, bioinformatics and neuroscience
  • Physical-technical sciences: computer graphics/vision, physics and geoscience
  • Human and social sciences: linguistics, cognitive science and social psychology

Our choice of specific application fields is not supposed to be exhaustive. Instead, we selected a relevant subset of scientific disciplines representing different characteristics and providing sufficient diversity in terms of methods and questions. These disciplines will serve as a “test bed” to help us advance machine learning for science in general by comparing and contrasting the success of different methods in various fields and distilling common problems or questions in different fields.