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University of Luxembourg
PhD PRIDE Fellow in Computational Engineering
University of Luxembourg
The University of Luxembourg aspires to be one of Europe’s most highly regarded universities with a distinctly international, multilingual and interdisciplinary character.
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Published: 3 months ago
Application deadline: Unspecified
Location: Luxembourg, Luxembourg
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PhD PRIDE Fellow in Computational Engineering

The University of Luxembourg (UL) and Luxembourg Institute of Socio-Economic Research (LISER) invite applications for a DRIVEN PhD Fellow (Doctoral Candidate) position (m/f) as part of the DRIVEN Doctoral Training Unit (, consisting of 19 doctoral candidates. DRIVEN is funded by the FNR PRIDE funding instrument

PRIDE PhD Fellow Ref: R-AGR-3440-13-C

  • 14 months fixed-term contract, renewable up to 3 years.
  • Full-time basis (40h/week).
  • Employee and student status.
  • Enrollment in the DRIVEN Doctoral Training School.
  • Position : 50013532


You will be working as part of DRIVEN Doctoral Training Unit (DTU) funded by the FNR PRIDE scheme. The Computational and Data DRIVEN Science DTU will train a cohort of 19 Doctoral Candidates 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. DRIVEN will build a bridge between state-of-the-art data driven modelling approaches and particular application domains, including Computational Physics and Engineering Sciences, Computational Biology and Life Sciences, and Computational Behavioural and Social Sciences.

Your Role

Research direction:

In the research direction “Data-driven identification of governing equations in continuum mechanics” within the field of Computational Mechanics, the Doctoral Candidate will address how the governing model equations can be systematically identified from observations of the system’s spatio-temporal activity. Computational data-driven methods for model identification in continuum mechanics based on (potentially incomplete, non-uniform and noisy) measurement data are expected to deliver the mathematical model of the observed problem as set of parameterised (nonlinear) PDEs. Here, identification of constitutive relations for smart materials in multiphysics energy harvesting problems is a possible focus direction. The PhD project will explore the opportunities and limitations of selectively learning from an existing library of canonical phenomenological constitutive models. Similar to an expert system, the most influential terms (linear or nonlinear) in a pool of governing equations and parameters are selected by regression analysis on available data. Based on an objective function on a selected output quantity the constitutive behaviour is described as a linear combination of phenomenological basis models. Evaluation and classification of the measurement data is followed by conceptualising PDE model-updating given the availability of new and possibly relocated data. The PDE-based model obtained for a specific objective is finally automatically fed into a high-performance predictive analysis tool (FEniCS, providing high-level abstraction layers for mathematical problem descriptions.


Your lead supervisor will be Prof. Andreas Zilian. Further supervision will be provided by Dr. Jack Hale and Prof. Stéphane Bordas.

Your primary tasks as a DRIVEN fellow are to:

  •  Manage and drive forward your research project.
  •  Attend doctoral school courses, trainings and social events.
  •  Write scientific articles and your PhD thesis.
  •  Disseminate your research at conferences and seminars
Your Profile
  •  Master's degree in Computational Engineering, Physics, Applied Mathematics, Civil or Mechanical Engineering.
  •  Profound knowledge of Continuum Mechanics, Materials Science, Applied Mathematics and Finite Element Methods
  •  Experience with Python/C++ programming and related software development environments.
  •  Good English language skills (a certified level B2 is the minimum required, while level C1 is a plus).
  •  Willingness to work in an inter-cultural and international environment.
  •  Ability to work independently and as part of a team.
  •  Curiosity and self-motivation.
We offer
  •  A dynamic and well-equipped research environment within the Department of Computational Engineering and Sciences.
  •  Intensive training in scientific and transferable skills, participation in schools. conferences and workshops.
  •  Fixed term employee contract totalling 48 months, subject to review, at UL.
  •  Enrollment as a PhD student in the DRIVEN Training Unit, within an appropriate the UL doctoral school, e.g.   Doctoral School of Science and Engineering.
  •  Personal work space at UL.
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