We are seeking an assistant professor with expertise in computational modeling of material behavior at multiple length scales, to join the research team of the SunRise FIS 2023 Project. This role focuses on developing and validation of advanced numerical models to simulate the mechanical performance, failure and crack propagation mechanisms, and residual stress effects in 3D-printed nano- and micro-structured materials and coatings.
The position involves the use of finite element modeling (FEM) tools, including cohesive element modeling and phase-field modeling, to investigate material behavior under various loading and environmental conditions, as well as interaction and cooperation with the experimental tasks for validation.
The successful candidate will play a key role in developing and validating computational tools to predict load-bearing capacity, failure modes, and interface reliability in coated 3D structures.
Doctoral Degree in Materials Science or Engineering, Aerospace or Mechanical Engineering, or related fields.
Essential:
- Demonstrated expertise in multi-physics finite element modeling (FEM) using software such as Abaqus.
- Proficiency in contact mechanics simulations, including modeling of interfacial effects and stress analysis.
- Proven experience with cohesive element modeling and/or phase-field modeling for fracture mechanics.
- Ability to simulate complex geometries and heterogeneous materials with high precision.
- Expertise in modeling residual stress effects on crack propagation in coatings, including eigenstrain theory.
- Programming skills in Python, MATLAB, or similar languages for pre- and post-processing of numerical models.
- Familiarity with mesh generation techniques and adaptive meshing for modeling complex microstructures and/or shapes.
Desiderable:
- Experience with multiphysics simulations and modeling coupled mechanical and thermal effects.
- Familiarity with microstructure-based modeling techniques to simulate real material geometries.
- Knowledge of machine learning approaches for accelerating computational modeling workflows.
- Experience in calibrating numerical models using experimental data (e.g., nanoindentation or microscopy-based measurements).
- Prior involvement in experimental validation of models, particularly for coatings and thin films.
- Strong publication record in high-impact journals and prior experience in grant-funded research projects.
- Demonstrated ability to collaborate with experimentalists and industrial partners in multidisciplinary research teams.
Offer ended on 17 Marzo 2025
