Scientist, Computational Sustainability, IAIC

We are looking for potential candidates to join a vibrant and collaborative team of scientists and engineers in the Computational Sustainability Division (CoS), Institute of Advanced Intelligence and Computing, A*STAR. The candidate is expected to contribute to research and development in computational fluid dynamics (CFD) addressing challenges in urban sustainability, marine-offshore decarbonisation, low-carbon energy, renewable energy, and other related areas. You will be working on R&D projects ranging from fundamental capability building to applied research, offering great opportunity for growth and impact.

The key scope of work includes:

• Developing modelling and simulation capabilities for multi-physics, multi-component, and multi-phase fluid flow problems.
• Developing Physics-Informed Machine Learning (PIML) models, which includes the foundation methodologies for incorporating the governing physics into the machine learning models.
• Developing physics-based data-driven surrogate modelling and data assimilation techniques for flow problems and applications.
• Working closely as a team to develop and apply CFD codes across various domains (e.g. environmental flows, hydrodynamic flow, turbulent flows, and dispersion modelling).
• Collaborate with industry partners, affiliated research institutes and other relevant stakeholders.

Job Requirements

• Strong background in physics and/or engineering; preferably holding a PhD degree in Mechanical, Aerospace, Civil, Environmental, Chemical, Computational Engineering, Applied Physics, or other relevant disciplines.
• Comprehensive understanding of physics and/or engineering principles, encompassing fluid dynamics, flow transport, thermodynamics, as well as expertise in multi-phase and multi-component flow.
• Deep knowledge in numerical methods (e.g., finite volume, lattice Boltzmann, volume of fluid) and high-performance computing.
• Experience in development of computational methods for example in usage and customization of open-source codes (e.g. OpenFOAM, Nek5000, Palabos) and expertise in optimization (e.g., linear, nonlinear, and real-time optimization) is an advantage.
• Proficiency in programming languages including but not limited to Python, C/C++, Fortran, CUDA, Julia.
• Experience with machine learning techniques such as neural networks, deep learning.
• Good interpersonal and communication skills, ability to adapt and work effectively as a member of a team, good command of written and spoken English, resourceful and self-driven with a high degree of professional integrity.