Postdoctoral Fellow – AI‑Based Surrogate Modeling for Reliability Optimization

About the role

The Wallenberg‑NTU Postdoctoral Fellowship invites outstanding early‑career researchers who earned their PhD at a Swedish university to join NTU for a two‑year research appointment. The fellow will work on cutting‑edge AI‑driven surrogate modelling for reliability‑based topology optimisation.

Key responsibilities

• Develop and implement decoupled AI‑based surrogate models for reliability‑based topology optimisation (RBTO).
• Design geometry‑generalising machine‑learning models and explainability‑driven active‑learning strategies for RBTO problems.
• Perform finite‑element simulations to support AI‑based surrogate frameworks.
• Validate methods using benchmark engineering case studies.
• Prepare technical reports, research publications, and documentation of developed methods.
• Present findings at internal meetings, conferences, and workshops.
• Collaborate with and supervise undergraduate and postgraduate students on related projects.

Required profile

• PhD in Mechanical Engineering, Civil Engineering, Computational Engineering or a closely related discipline.
• Strong background in reliability engineering, uncertainty quantification and optimisation methods.
• Experience with surrogate modelling, machine learning or AI applied to engineering problems.
• Excellent academic writing and communication skills.
• Ability to work independently and as part of an interdisciplinary research team.

Required skills

• Programming in Python, MATLAB or C++.
• Finite‑element analysis and numerical simulation.
• Surrogate modelling and machine‑learning techniques.
• Active‑learning and explainable‑AI methods.
• Probability, statistics and optimisation algorithms.