Phd Position

The development of complex materials with tailored properties, as well as the understanding of the underlying mechanisms require reliable high-throughput simulation tools, that can be combined with data analytics and material characterization. This will allow to predict the structure-property relation and to understand and evaluate experimentally obtained data sets. As a PhD student you will work at the Institute for Advanced Simulations - Materials Data Science and Informatics (IAS-9) that combines data analysis, materials simulation, research data management and software development under one roof. It also benefits from a strong connection to the Ernst-Ruska-Centre for Electron Microscopy and to the Jülich Supercomputing Center. As such, it provides a multidisciplinary environment during your research that will help to develop your competencies in a range of scientific fields.

**Your Job**:
Digital methods for inverse materials design are essential to efficiently create new, sustainable and recycling-adapted structural metals. Alloys with a reduced number of elements, so-called lean alloys, and material systems with a high tolerance to impurities from the use of secondary raw materials are of particular relevance for improving recyclability and sustainability. Al-Ca is a promising lean alloy system for additive manufacturing, as the mechanical properties can be tailored through phase composition, distribution and morphology by tuning process parameters. The work is carried out within the DFG Priority Programme “DaMic - Data-driven Alloy and Microstructure Design of Sustainable Structural Metals” (SPP 2489), in close collaboration with a research partner responsible for the manufacturing and characterization of material samples.

As such, the position offers the opportunity to be involved in fruitful national collaborations. In this exciting job you can expect:

- to develop automated workflows for descriptor based microstructure reconstruction
- to identify material parameters for crystal plasticity simulations from experimental data through inverse analysis
- to establish structure-property linkages based on numerical simulations and to transform them into AI- and ML-ready information
- to develop and implement an indirect inverse optimization framework to identify microstructures that exhibit desired macroscopic properties
- to publish and present research results in relevant journals and at international conferences

**Your Profile**:

- A completed university degree (Master or equivalent) with excellent grades in the field of mechanical engineering, material science, physics, computational science or similar, with experience in modeling and simulation
- Strong understanding of the (computational) mechanics of solids and the finite element method and/or spectral solvers
- Practical experience in at least one programming language (preferably Python) and experience with the use of Unix/Linux operating systems
- Structured and analytical thinking as well as a systematic, careful, independent, and reliable working method
- Interest in working closely with experimentalists
- Strong cooperation and communication skills and the ability to work as part of a team
- Excellent written and spoken English skills

**Our Offer**:
We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change We offer ideal conditions for you to complete your doctoral degree:

- The opportunity to conduct exciting research in an international and multidisciplinary environment with outstanding infrastructure
- The opportunity to gain your reputation in a dynamic and highly active research field
- Part-time position with 19,5 hours per week and flexible working hours
- 30 days of annual leave (depending on agreed working time arrangements)

Targeted services for international employees, e.g. through our International Advisory Service

Place of employment: Aachen/Jülich