Esch-sur-Alzette, 4 October 2018 - The University of Luxembourg in collaboration with the public research centres LIST (Luxembourg Institute of Science and Technology) and LISER (Luxembourg Institute of Socio-Economic Research) have recently launched the Doctoral Training Unit (DTU) on Data-driven Computational Modelling and Applications (DRIVEN). With a budget of 3.4 million euros over six years, this pioneering programme will train a new generation of early-stage researchers with the required knowledge and skills to tackle complex data-intensive problems in all sectors of the economy.

Third pillar of scientific investigation

Understanding the world, which grows more complex and generates data at an increasing rate, relies on the ability to construct robust and reliable models. Together with theory and experimentation, Computational and Data Sciences have become the third pillar of scientific investigation. Nowadays, it underpins the majority of technological, economic and societal feats. “We have entered an era in which soaring amounts of data offer enormous opportunities, but only to those who are able to harness them. We are standing at a turning point where the economic success of a nation is determined by its ability to exploit the vast amounts of information daily generated”, explains Prof. Andreas Zilian, coordinator of the DTU DRIVEN at the University of Luxembourg. 

Develop common data-driven approaches

The specificity of the DTU DRIVEN lies in its interdisciplinary as it covers a wide range of fields, from hard sciences to human sciences, including economics, and gathers researchers from 3 doctoral schools (DSSE, DSHSS, DSEF) and 3 institutions (Uni.lu, LIST, LISER). Thus, the main challenge is to develop approaches common to a number of applications in various research areas. “Within the DTU, PhD students will acquire a solid data science background to use and develop cross-disciplinary data-driven modelling approaches. Based on machine learning algorithms, data-driven modelling enables to identify new relations in data, re-assess, reformulate and enhance previously established understanding”, says Prof. Zilian.

Three research groups