ENAC - PhD Position in Physics-Informed Machine Learning For Mechanics of Material Failure (ML)

WorkplaceLausanne, Lake Geneva region, Switzerland

ENAC - PhD Position in Physics-Informed Machine Learning For Mechanics of Material Failure (ML)

The Data-Driven Mechanics Laboratory is seeking a highly motivated doctoral student to study physics- and thermodynamics-informed machine learning (ML) for the mechanics of material failure. The project focuses on learning the effective behavior of materials of civil engineering interest (granular geomaterials, structural materials) arising from complex microstructural mechanisms involving inelasticity and friction, as well as potential multiphysics couplings. It promises to push the boundaries of constitutive and multiscale modeling for these materials.

Main roles and responsibilities:

Research in this project involves a synergistic combination of numerical modeling, and physics- informed ML with the following key aspects:

  • Data mining through micromechanical/mesoscale simulations (Discrete/Finite Elements).
  • Collaboration with experimentalists towards validation of the simulations.
  • Development and implementation of the machine learning framework.
  • Dissemination in journals and international conferences.


  • Passion about working at the intersection of mechanics and machine learning.
  • Bachelor/Master’s degree in Civil Engineering, Mechanical Engineering, Applied Mathematics, Computational Science or related areas.
  • Background in computational mechanics, experience in scientific machine learning.
  • Proficiency in Python, and familiarity with low level languages (e.g. C++).
  • Excellent English communication skills (oral and written).

We offer:

  • World-class multi-cultural environment on the shores of beautiful Lake Leman.
  • Innovative interdisciplinary research at the Data-Driven Mechanics Lab.
  • Competitive salary and employment conditions.

Application procedure:

Please email asingle PDF consisting of:

  • Curriculum vitae (max. 2 pages) that includes contact details of at least 2 referees,
  • Motivation letter (max. 1 page),
  • Detailed transcript of records (both Bachelor’s and Master’s)

to [email protected] , indicating in the subject "PhD Application PI-ML - Your Name" until July 15th 2024. Applications will be evaluated in the order that they are received. For inquiries please contact Prof. Kostas Karapiperis at the same email.

ExpectedStartDate: Fall/Winter 2024

Duration: 4 years (1-year fixed-term contract renewable annually according to EPFL rules)

In your application, please refer to myScience.ch and reference JobID 64542.

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