Data Science & Engineering



Research and innovation conducted across the EuroTech Universities is growing fast, covering the whole Big Data value chain, which include advances in core techniques, technologies and infra- structure in data collection, storage and management, data analysis, controlled and effective data sharing, smart manufacturing and life science. The digitalization of society, industry and businesses has radically transformed the ways in which we live and work. Computer Science and Engineering are the enablers of this transformation, creating the foundation for new business models and businesses that have grown at unprecedented rates. Hence, collecting, storing, preserving, managing, analysing, and sharing exponentially increasing quantities of data present a variety of significant Big Data challenges that research must address.
Moreover,  the EuroTech Universities conduct research and collaborate in Key Enabling Technologies such as Photonics and Artificial Intelligence (AI).



The Alliance has highlighted its expertise in Data Science & Engineering on a number of occasions:





The collaboration of EuroTech Universities research groups focuses in particular on the intersections of Big Data and Health and Big Data and Transport. A number of workshops have been hosted and co-organised by the Brussels Office:


The EuroTech Universities are  exploring synergies between their individual curricula in Data Management and Data Science. This could possibly lead to more enhanced cooperation and joint curriculum development across all educational levels.

  • EuroTech Winter School on Cross-Disciplinary Perspectives on Machine Learning
  • EuroTech Summer School on Computing Resilience (Urban Resilience)
  • EuroTech Advanced School on Immersed Methods
    The workshop for PhD students from the EuroTech universities took place in November 2017 at the Eindhoven Multiscale Institute, TU/e. The Advanced School on Immersed Methods provided  participants with a comprehensive overview of the field of immersed methods (e.g., the finite cell method, cutFEM, immersogeometric analysis) and their applications in fluid and solid mechanics. Immersed methods are powerful tools for solving mechanical and multi-physics problems on complex domains. The pivotal idea of these methods is to embed the complex physical domain in a larger embedding domain of simple geometry, which allows for structured meshes on which the solution fields are interpolated using higher-order bases.

Photo: Eric Berghen



Key Experts Involved

Big Data

DTU - Bjarne Kjær Ersbøll, DTU Compute
EPFL - Daniel Kressner, Numerical Algorithms and High Performance Computing
TU/e - Alessandro Di Bucchianico, Chair of Probability and Statistics
TUM - Massimo Fornasier, Chair for Applied Numerical Analysis, Department of Mathematics


DTU - Lars-Ulrik Aaen Andersen, Head of Department of Photonics Engineering
EPFL - Fabien Sorin, Head of Laboratory of Photonic Materials and Fibre Devices
TU/e - Ton Backx, CEO Institute for Photonic Integration and Photon Delta
TUM - Jonathan Finley, Chair of Semiconductor Nanophysics; Gerhard Kramer, Chair of Communications Engineering

Image: Fotolia




Brussels Office: Émilie Né