Vacancy No. 1166/2024

Research Associate/ Doctoral Student (f/m/d)

Job description

The Institute for Concrete Structures and Building Materials, Department of Building Materials and Concrete Construction, is headed by Univ.-Prof. Dr.-Ing. Frank Dehn and is among the leading German institutions on material testing and research in the field of concrete constructions. You will be part of the research group Numerical modelling and Digitalization for Building Materials and Concrete Structures headed by Dr. Ravi Patel. This group engages in a wide range of topics, including nonlinear finite element modelling, multiphysics and multiscale modelling, thermodynamic modelling, reactive transport modelling, image-based modelling, and machine learning applications such as damage detection, data-driven mix design, and physics-informed machine learning. Topics also include the digitalization of laboratory data and data management. The working group collaborates closely with other research groups within the institute to conduct experiments needed to develop and validate the models. This collaboration provides a unique work environment that bridges fundamental theoretical modelling and experimental research.

Your main tasks will include supporting and developing research on machine learning applications in concrete technology, focusing on areas such as data-driven mix design, generative AI applications in concrete technology, and physics-informed machine learning to accelerate numerical simulations. You will work in close collaboration with colleagues who perform experiments and develop coupled thermo-hydro-chemo-mechanical models for concrete technology. You will have opportunities to further develop your skills in this field to achieve doctoral qualification. Additionally, you will have the chance to conduct experiments, participate in other research activities closely related to your topic, and assist in teaching and consulting duties.

Personal qualification
  • Masters in civil engineering, mechanical engineering, material science, mathematics or similar
  • Already gained fundamental understanding of machine learning and data driven modelling topics such as Bayesian optimization, Physics Informed Neural Networks, Generative AI, etc. demonstrated through master thesis or internships.
  • Demonstrated experience in programming with C++, python, MATLAB or Fortran and use of libraries such as PyTorch, TensorFlow, etc.
  • Familiar with use of Linux based system and version controlling using git.
  • Fluent spoken and written English and preferably good command on spoken and written German.
  • Experience with programming numerical methods for solving PDEs such as FEM and FVM is plus.
  • Experience with field of concrete technology or porous media is plus.
  • Experience with HPC such as Cuda computing, MPI
  • parallelization is plus.

Salary category 13 TV-L, depending on the fulfillment of professional and personal requirements.

Organizational unit

Institute of Concrete Structures and Building Materials (IMB) - Material Testing and Research Institute (MPA Karlsruhe)

Starting date

1 August 2024 or earliest possible date after mutual agreement

Contract duration

Initially limited to three years with potential for extension towards achieving a PhD on the topic of the research.

Application up to


Please send a CV, transcript of records from your Master studies and a short motivation letter (max. 1 page) stating your research interests.  If you have written a Master’s thesis involving finite element method, numerical modelling or Multiphysics model please feel free to share the file (max. 10 MB).

Contact person in line-management

For further information, please contact Dr. Patel.


Please apply online using the button below for this vacancy number 1166/2024 .
Personnel Support is provided by 

Ms Carrasco Sanchez
phone: +49 721 608-42016,

Kaiserstr. 12, 76131 Karlsruhe

We prefer to balance the number of employees (f/m/d). Therefore we kindly ask female applicants to apply for this job.
Recognized severely disabled persons will be preferred if they are equally qualified.