The Doctoral Training Unit (DTU) on Data-driven Computational Modelling and Applications (DRIVEN) trains cohorts of Doctoral Candidates who develop data-driven modelling approaches common to a number of applications strategic to the Luxembourgish Research Area and Luxembourg’s Smart Specialisation Strategies. The DTU creates a bridge between strong methodological core competencies and application domains by training each Doctoral Candidate both in state-of-the-art data-driven approaches, and in the particular application domain in which these approaches are expected to lead to new discoveries: Computational Physics and Engineering, Computational Biology and Life Sciences, Computational Behavioural and Social Sciences.
Funded by the Fonds National de la Recherche (FNR), DRIVEN will result in a group of scholars that enriches Luxembourg’s socio-economic landscape not only with expertise in data-driven discovery and machine learning, but also with a fundamental understanding of how these approaches can be of most use to a wide range of focus areas. We are strengthening the data-driven repertoire in areas already benefiting from these techniques, and strive to establish similar techniques in areas where these approaches are only nascent.
By embedding DRIVEN in the existing Doctoral Schools of the University of Luxembourg, a transversal Doctoral Programme becomes available, spanning across Faculties and reaching out to the Interdisciplinary Centres, as well as to the public research centres LIST and LISER. DRIVEN benefits from an existing doctoral education framework and established best practices, allowing to focus on innovative doctoral training strategies for its highly interdisciplinary research directions.
DRIVEN contributes, in conjunction with strong national and European initiatives such as Digital Lëtzebuerg and the Important Project of Common European Interest on HPC and Big Data Enabled Applications, to boosting Luxembourg’s competitiveness thanks to an increased ability to make use of the vast amount of data generated worldwide on a daily basis.