Student Project / Master’s Thesis (f/m/div) – Data-Driven Constitutive Modeling for Computational Mechanics - #2719337

The Max Planck Institute for Intelligent Systems (MPI-IS) conducts research to advance intelligent systems that perceive, learn, and interact. Through fundamental research across computational, physical, and social dimensions, we shape artificial intelligence and robotics to advance knowledge and benefit society.

We are looking for a

Student Project / Master’s Thesis (f/m/div) – Data-Driven Constitutive Modeling for Computational Mechanics

The Mechanics for Intelligent Structures research group, led by Dr. Renate Sachse at the Max Planck Institute for Intelligent Systems (MPI-IS), is looking for a motivated student to work on data-driven constitutive modeling for nonlinear computational mechanics. The project combines mechanics, machine learning, and scientific computing, with the goal of developing learned material models that can be directly integrated into finite element research software.

Roles & Responsibilities

Constitutive models describe the mechanical response of materials and are a central component of finite element simulations. While classical approaches rely on predefined mathematical formulations, data-driven approaches provide new possibilities to learn complex material behavior directly from data.

In this project, the student will investigate how material behavior can be learned from experimental data and represented by a data-driven constitutive model. A particular focus will be on the integration of the resulting model into research software for computational mechanics, allowing its performance to be assessed in nonlinear finite element simulations.

Typical tasks include:

• generating and/or processing data for training and validation,
• developing and training data-driven constitutive models,
• incorporating mechanical principles and physical constraints into the learning approach,
• implementing the developed models in Julia,
• integrating the constitutive model into an existing finite element research software framework,
• testing and validating the model on nonlinear material and structural mechanics problems,
• comparing the learned model with established constitutive formulations,
• evaluating accuracy, robustness, generalization, and computational efficiency.
  
  

The exact scope of the project will be adapted to the student’s background and interests.

Education & Experience

Applicants should

• be enrolled in a Master’s program in mechanical engineering, civil engineering, aerospace engineering, computational engineering, applied mathematics, physics, or a related field,
• have a solid background or strong interest in continuum mechanics, computational mechanics, and numerical methods,
• have programming experience,
• have an interest in machine learning and data-driven modeling,
• ideally have prior experience with finite element methods, constitutive modeling, or machine learning,
• be able to work independently and reliably,
• have good oral and written communication skills in English.
  
  

Our offer

• A well-defined research project at the interface of computational mechanics and machine learning
• Hands-on experience in data-driven constitutive modeling and nonlinear finite element analysis
• The opportunity to contribute to research software developed and used within the group
• Close interaction with PhD students and the group leader
• Access to the research environment and computational resources of the Max Planck Institute for Intelligent Systems
• The possibility of an accompanying paid student assistant (HiWi) position. Students holding a Bachelor’s degree are currently paid €15.61 per hour. Contracts are typically issued per semester.
  
  

The project can be carried out as a Master’s thesis or another student research project. A combination with a student assistant position may be possible depending on the project setup and applicable employment regulations.

Application

Applications should include the following documents (in English):

• Cover letter (maximum two pages), describing the applicant’s academic background and interests, as well as their motivation for the project and relevant experience in computational mechanics, machine learning, and/or software development.
• Curriculum vitae, including a summary of education, research experience, relevant skills, and (if applicable) publications and software contributions
• Transcript(s) of records from current and previous degree programs
  
  

Application without these documents including the letter will not be considered.

Please send the requested documents in asingle PDF file, via the

application portal

• The posting is open until filled. For further information about the position, please contact Dr. Renate Sachse at
  
  

[email protected]

• The Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. The Max Planck Society strives for gender equality and diversity. Furthermore, the Max Planck Society seeks to increase the number of women in its workforce in those areas where they are underrepresented and therefore explicitly encourages women to apply.
  
  

Closing date for applications

The posting is open until filled.