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), ASTAR 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. The above eligibility criteria are not exhaustive. ASTAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.