Data-driven computational modelling and applications

The Computational and Data DRIVEN Science Doctoral Training Unit (STU) will train a cohort of Doctoral Candidates (DCs) who will develop data-driven modelling approaches common to a number of applications strategic to the Luxembourgish Research Area and Luxembourg's Smart Specialisation Strategies. We propose to crate this bridge between a methodological core and application domains by training each DC both in state-of-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 Sciences, Computational Biology and Life Sciences, and Computational Behavioural and Social Sciences. In six years, 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 will strengthen the data-driven repertoire in areas already benefiting from these techniques, and will strive to establish similar techniques in areas where these approaches are only nascent. By embedding the DRIVEN DTU in the existing Doctoral School (DS) structure of the University of Luxembourg, we will create its first transversal Doctoral Programme, spanning all three Faculties and reaching out to the Interdisciplinary Centres, the LIST and LISER. DRIVEN will benefit from the already established doctoral education framework and best practices gleaned from previous DTUs, allowing our DTU to focus on innovative doctoral training strategies for its highly interdisciplinary research directions. DRIVEN will contribute, in conjunction with strong national and European initiatives such as Digital Lëtzebuerg and the Imprtant 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.