This collaborative doctoral project brings together the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will play a central role in this interdisciplinary initiative. They will:
- Develop and apply machine learning (ML) methods—including surrogate modeling, feature extraction, and inverse design algorithms
- Generate synthetic microstructures (based on the open source OptiMic software)
- Perform descriptor extraction and micromechanical simulations (MCRpy, DAMASK)
- Vary the material processing parameters, which results in materials with diverse microstructures and mechanical properties
- Perform experimental characterizations of additive-manufactured and heat-treated steels, using state-of-the-art methods such as scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), X-ray diffraction (XRD), and nanoindentation, for generating their own data sets
Table of Content
Summary
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Benefits
- A creative work environment at a leading research facility, located on an attractive research campus at the TZA Aachen https://tza-aachen.de and the Forschungszentrum Jülich
- The opportunity to gain your reputation in a dynamic and highly active research field
- Further development of your personal strengths, e.g. through an extensive range of training courses; a structured program of continuing education and networking opportunities specifically for doctoral researchers via JuDocS, the Jülich Center for Doctoral Researchers and Supervisors: https://www.fz-juelich.de/en/judocs
- Flexible working hours and various opportunities to reconcile work and private life-life, such as the option of slightly reduced working hours and 30 days of annual leave
- Targeted services for international employees, e.g. through our International Advisory Service
- Opportunity to participate in (international) conferences and project meetings
- Continuous professional support from your scientific supervisors
In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer: https://go.fzj.de/benefits
Requirements
- A completed university degree (Master or equivalent) with excellent grades in the field of data science, material science, mechanical engineering, physics, or similar, with a strong Machine Learning or simulation background
- In depth practical experience in at least one programming language (preferably Python)
- Ideally, some practical experience in material characterization methods
- Structured and analytical thinking as well as a systematic, careful, independent, and reliable working method
- Strong cooperation and communication skills and the ability to work as part of a team
- Excellent written and spoken English skills
Application Documents
- Motivation letter,
- CV, and
- University degree certificates and grade transcripts.
Check also:
2026 Fully Funded Humboldt Research Fellowship
2026 Konrad Adenauer Foundation Scholarship
Application Deadline
Not SpecifiedHow To Apply
Are you qualified and interested in this opportunity? Kindly go to
Forschungszentrum Jülich on www.fz-juelich.de to apply
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For more information, kindly visit Forschungszentrum Jülich schoalrship webpage.
