Postdoctoral Research Fellow Full-time

Postdoctoral Research Fellow

Are you a PhD graduate in Structural or Mechanical Engineering looking to push the boundaries of computational mechanics, fracture modelling, and experimental validation? Join us for a targeted 12-month research initiative bridging high-performance computing (HPC), advanced ABAQUS finite-element modelling, and hands-on laboratory testing.

Reporting to a Professor in Structural Engineering, you will lead the development and validation of multi-scale fracture mechanics models. In this role, you will bridge numerical simulation with physical experiment, developing automated Python workflows, running parametric studies on HPC clusters, and validating predictions against experimental results.

What You'll Do

• Build, verify, and validate multi-scale models using ABAQUS linked with user-defined material/element subroutines, writing Python scripts for simulation automation, data processing, and workflow optimisation.
• Train ML models based on published literature, experimental data, and FEA results to support optimization of advanced materials.
• Support mechanical testing (setup, instrumentation, and data acquisition) to validate numerical predictions and contribute to machine learning and optimisation activities.
• Provide technical guidance and scripting support to PhD students, co-author journal manuscripts, and contribute to research funding applications.
• Drive the project toward key deliverables, including a reusable modelling workflow, a structured simulation dataset, and top-tier journal submissions.

What You Will Bring

• A PhD in Structural Engineering, Mechanical Engineering, or a closely related discipline.
• Proven expertise in finite-element modelling with ABAQUS, grounded in solid and fracture mechanics (e.g. multi-scale modelling of composite laminates or concrete, phase-field fracture modelling) and experience with ML models.
• Strong Python skills for automation, data analysis, and workflow tool development, including experience with ML and optimisation.
• Excellent communication skills, a collegiate mindset, and a strong track record of producing clear reports and publications.
• Experience with experimental testing using mechanical loading machines.