Scientist (AMS), IHPC

About the Job

We are looking for motivated candidates to join a vibrant and collaborative team of scientists and engineers in the Advanced Manufacturing & Semiconductor Division (AMS) at the Institute of High Performance Computing (IHPC), A.STAR. The successful candidate will contribute to research and development in physics-based modelling and scientific machine learning for semiconductor packaging reliability and multiphysics systems. This research explores new computational paradigms that integrate finite-element modelling, multiphysics simulations, and machine learning techniques to enable predictive modelling and accelerated analysis of complex physical systems relevant to advanced semiconductor packaging technologies. These include thermo-mechanical reliability of electronic packages, stress evolution in heterogeneous material systems, interfacial failure mechanisms, and processstructureproperty relationships in packaging materials and architectures. You will work on R&D projects spanning fundamental methodology development and application-driven research, with opportunities to collaborate with interdisciplinary teams and industrial partners in the semiconductor ecosystem.

The key scope of work includes

• Integrating finite-element simulations and physics-based models with machine learning approaches for predictive reliability analysis.
• Developing data-driven surrogate models and reduced-order models for thermo-mechanical behaviour in electronic packaging structures.
• Developing high throughput computational workflows that combine multiphysics simulations, data analytics, and machine learning techniques.
• Contributing to the development of AI-enabled predictive frameworks for semiconductor packaging reliability and performance.
• Publishing research outcomes in leading journals and conferences in computational mechanics, semiconductor packaging reliability, and scientific machine learning.
• Collaborating with internal research teams, industry partners, and affiliated institutes on interdisciplinary R&D projects.

Job Requirements

• PhD degree in Mechanical Engineering, Computational Mechanics, Applied Mathematics, Computational Physics, or related disciplines.
• Strong background in numerical simulation and multiphysics modelling, particularly finite-element modelling of thermo-mechanical processes.
• Experience in modelling mechanical and thermal behaviour of materials and structures.
• Experience in data-driven modelling or machine learning approaches for physical systems.
• Strong programming skills in Python, MATLAB, or FORTRAN, with experience in scientific computing environments.
• Experience with simulation tools such as ABAQUS, ANSYS, COMSOL, or other multiphysics simulation platforms is advantageous.
• Experience with high-performance computing or large-scale simulations is an advantage.
• Strong analytical and problem-solving skills, with the ability to work both independently and collaboratively.

We particularly welcome early-career researchers who are passionate about advancing AI-driven modelling and predictive simulation technologies for semiconductor packaging reliability and multiphysics engineering systems.