RESEARCH FIELDS

The DRIVEN research fields are Economics, Environmental Engineering, Geophysics, Material Engineering, Physics, Psychology, Social Sciences, Life Sciences and Systems Biomedicine. They are represented by PIs from various Luxembourgish research institutions and clustered in three groups:

• Computational Physics and Engineering Sciences,
• Computational Biology and Life Sciences,
• Computational Behavioural and Social Sciences.

Machine learning algorithms and Big Data concepts are already exploited in some of these research fields. DRIVEN substantially enhances existing understanding and algorithms in these fields and explores the utility of newly emerging methods. In those research fields where data-driven techniques are less established, we explore, adapt and test the utility of established methods and demonstrate their benefits for generating new insights and research questions. These activities greatly benefit from knowledge exchange between research fields, enabling an appreciation of the full potential and limitations of data-driven approaches.

INDICATIVE PhD RESEARCH DIRECTIONS

Abstraction, methodological, data source and output clustering of DRIVEN’s 19 Research Directions. The disks represent the Research Directions of each of the three clusters. Overlaps between circular and rectangular shapes indicate commonality in methods, data sources, abstraction and outputs. This schematic representation of the principal research themes and sub-themes of DRIVEN highlights the multidisciplinary context in which the Doctoral Candidates will be trained.

INTERDISCIPLINARY

DRIVEN aims to create a new generation of early-stage researchers with the confidence and skills to tackle complex data-intensive problems found in public institutions, industrial R&D and academia. Here, our guiding principle is that applications drive fundamental research and innovation. The interdisciplinarity of the DTU allows to demonstrate how application of data-driven discovery methods is realised in the various scientific fields, represented by the PIs, clustered research lines and partner institutions, with their characteristic data and analysis/prediction objectives.