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.
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.
