Job description
We are looking for potential candidates joining a vibrant and collaborative team of scientists and engineers in the Fluid Dynamics Department, 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 opportunities for growth and impacts.
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 techniques for flow problems and applications.
- Working closely as a team to develop CFD codes and apply them across various domains (e.g. environmental flows, reacting 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, Chemical, Aerospace, Civil, Environmental, Computational Engineering, Applied Physics, or other relevant disciplines.
- At least 3 years of experience
- Comprehensive understanding of physics and/or engineering principles, encompassing fluid dynamics, flow transport, thermodynamics, as well as expert in multi-phase and multi-component flow.
- Deep knowledge in numerical methods (e.g., finite volume, lattice Boltzmann, volume of fluid).
- 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, 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, strong communication and excellent writing skills, 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.
