Research Engineer ? Scientific Machine Learning / Physics Simulation (AMS)

We are looking for motivated candidates to join the Advanced Manufacturing & Semiconductor Division (AMS) at the Institute of High Performance Computing (IHPC), A.STAR as a Research Engineer. The candidate will support research and development in AI for Science and scientific machine learning, with a focus on implementing and scaling Physics Foundation Models (PFM) for modelling complex physical systems in advanced manufacturing and semiconductor applications.

This work involves integrating neural operators, graph-based numerical solvers, and deep learning architectures to develop next-generation computational modelling tools. You will work closely with researchers and engineers to build robust computational frameworks and accelerate physics-based simulations using machine learning.

The key scope of work includes:

• Implementing and optimizing neural operator architectures for modelling PDE-governed physical systems.
• Developing graph-based representations of numerical solvers and integrating them into machine learning frameworks.
• Building and maintaining machine learning pipelines for scientific simulations, including data generation, training, and evaluation.
• Integrating deep learning models with physics-based simulation workflows.
• Optimizing computational performance using GPU acceleration and high-performance computing platforms.
• Supporting the development of research prototypes and experimental platforms.
• Collaborating with interdisciplinary teams working on machine learning, physics, and engineering applications.

Job Requirements:

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Computational Physics, Applied Mathematics, or related disciplines.
• Strong programming skills in Python, with experience in deep learning frameworks such as PyTorch or JAX.
• Experience with scientific computing, numerical simulation, or PDE-based modelling is desirable.
• Familiarity with graph neural networks, neural operators, or physics-informed machine learning is an advantage.
• Experience with GPU computing, parallel computing, or high-performance computing environments is a plus.
• Good software engineering practices, including version control and reproducible research workflows.
• Strong problem-solving skills and ability to work effectively in a collaborative research environment.

We welcome engineers who are eager to build impactful technologies at the intersection of machine learning, physics, and advanced manufacturing.