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 High Performance 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 with atleast 5 years of relevant experience.
• 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, resourceful and self-driven with a high degree of professional integrity.

The above eligibility criteria are not exhaustive. A*STAR 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.