Tackling big data

As a result of digitalization, research is producing ever larger and more complex data sets. While these hold great potential for example for biomedicine, energy research, geo-research or robotics, they also need to be managed and interpreted. To address this need, the Munich School for Data Science @ Helmholtz, TUM & LMU (MuDS) has been established to train the next generation of researchers, who will tackle ‘big data’ problems. Over the next six years, the new graduate school will receive a total of twelve million euros in funding.

The school was founded by LMU Munich, Helmholtz Zentrum München, Max Planck Institute for Plasma Physics, German Aerospace Center and the Technical University of Munich (TUM). The Leibniz Supercomputing Center and the Max Planck Computing & Data Facility, two major computing and data centers in the Munich region, are also associated.

The aim of the Munich School for Data Science is to combine training in methodological aspects with training in application domain areas, namely biomedicine, plasma physics, robotics and earth observation, to educate the next generation of data scientists.

The Munich School for Data Science will offer joint projects for PhD students, each designed by two partners – a domain-specific application partner and a methodological partner. This will ensure that candidates receive methodological as well as application-specific training. In addition, participants will have the option of taking a course tailored to their needs, with a detailed onboarding phase followed up by advanced-level training. The training program will be integrated into existing courses provided by the universities as well as by the associated partners, thus guaranteeing up-to-date, high-level training. MuDS will operate under the umbrella of the partner institutes’ highly successful graduate schools.

“The Munich School for Data Science addresses one of the core challenges facing science today,” explains Professor Matthias Tschöp, CEO of the Helmholtz Zentrum München. “We need young talents with exceptionally skills in handling large data sets, who also possess an understanding of how scientific data is collected. With our approach, we aim to train them to become experts in artificial intelligence and machine learning, qualified to look beyond their own specialist discipline.”