Postdoctoral Researcher (m/f/d) | Interpretable Machine Learning for Phase-Field Models

Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg

Type of Job


Solid State Research & Material Sciences Complex Systems Computer Science Mathematics

Job Code: Postdoc2022_PF

Job offer from November 22, 2022

The Computational Methods in Systems and Control Theory group at the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg is inviting applications for the position of a Postdoctoral Researcher (m/f/d) in Interpretable Machine Learning for Phase-Field Models.

Job description

The Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany, seeks a postdoctoral researcher to carry out an interdisciplinary research project towards interpretable machine learning for phase-field models using experimental data obtained from scanning transmission electron microscopy. Phase-field models explain the evolution of microstructures with complex morphologies. Learning such models is challenging due to complex interfaces between different phases and often requires prior empirical knowledge and assumptions by practitioners. This project aims at learning phase-field models by innovative synergies between machine/deep learning and dictionary-learning tools. Combining prior knowledge by practitioners is essential to improve the efficiency of learning methodologies.  An ideal candidate has some experience with data-driven modeling, implementing deep/machine learning methods, and/or analysis and numerical solution of phase-field models. The start is as soon as possible and the runtime is one year from the joining date with a possible extension of one more year.

Expected requirements

The candidate is expected to fulfill the following requirements:

  • Ph.D. degree or equivalent (obtained or to be available soon) in Applied Mathematics, Computational Mathematics, Computational Science and Engineering, Material Science
  • Strong interest in theory, modeling, and simulation of physical and dynamical processes
  • Prior research experience in phase-field modeling is desired
  • Excellent programming proficiency in Python and hands-on experience in using deep learning frameworks (e.g. PyTorch, TensorFlow)
  • Strong interpersonal skills, such as written and oral communication skills in English

We offer

A warm working atmosphere in an international and interdisciplinary team with challenging research questions and access to high-quality software and hardware resources. Our working language is English. The successful candidate receives a standard working contract with payment and benefits in accordance with a public collective agreement (TVöD). Your salary is based on TVöD E13.


Please send a cover letter, CV, a short summary of your Ph.D. thesis, and at least two letters of recommendation. All the applications that are sent until Dec 10, 2022 will be given full consideration.
Please send your application via email (subject: Postdoc2022_PF_yourlastname) to .

For further details about the job profile, please contact , team leader “Physics-Enhanced Machine Learning" within the CSC department.

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. Furthermore, the Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and, therefore, explicitly encourages women to apply.

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