About the role
AI summarisedJoin the Computational Sustainability Division at IHPC A*STAR to develop advanced CFD and Physics-Informed Machine Learning frameworks for urban sustainability and decarbonization research.
ResearchOnsite
Key Responsibilities
- Develop advanced modelling and simulation capabilities for multi-physics, multi-component, and multi-phase fluid flow problems
- Design and implement Physics-Informed Machine Learning models and methodologies for embedding physical principles into ML frameworks
- Develop physics-based, data-driven surrogate models and data assimilation techniques for flow-related applications
- Apply CFD codes across diverse areas such as environmental flows, hydrodynamics, turbulent flows, and dispersion modelling
- Collaborate with multidisciplinary teams and industry partners to translate research outcomes into real-world impact
- Develop and customize open-source CFD codes for specific research and development projects
Requirements
- PhD in Mechanical, Aerospace, Civil, Environmental, Chemical, Computational Engineering, Applied Physics, or a closely related discipline
- Solid understanding of fluid dynamics, transport phenomena, and thermodynamics with expertise in multi-phase flows
- In-depth knowledge of numerical methods for fluid flow simulations including finite volume and lattice Boltzmann methods
- Experience with high-performance computing and customizing open-source CFD codes like OpenFOAM, Nek5000, or Palabos
- Proficiency in programming languages such as Python, C/C++, Fortran, CUDA, and/or Julia
- Experience with machine learning techniques, including neural networks and deep learning
- Strong interpersonal and communication skills with an excellent command of written and spoken English