Research Scientist (CoS Division) , IHPC

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 70,000 - 100,000; Full time

Job summary

A leading research institute in Singapore is seeking a candidate for the Computational Sustainability Division to work on advanced research in computational fluid dynamics. The role requires a strong PhD background in relevant engineering or physics disciplines, with knowledge of numerical methods and programming expertise. Responsibilities include developing models and collaborating on applied research projects in sustainable urban and marine systems. This position presents significant opportunities for professional growth and impactful contributions.

Qualifications

• PhD degree in Mechanical, Aerospace, Civil, Environmental, Chemical, or Computational Engineering.
• Understanding of fluid dynamics, thermodynamics, and flow transport.
• Experience with open-source computational methods and optimization.

Responsibilities

• Develop modeling and simulation capabilities for multi-physics problems.
• Create Physics-Informed Machine Learning models.
• Collaborate with industry partners on CFD applications.

Skills

Strong background in physics

Engineering principles

Experience in numerical methods

Proficiency in Python

Machine learning techniques

Good communication skills

Education

PhD degree in relevant disciplines

Tools

OpenFOAM

Nek5000

CUDA

Job description

We are looking for potential candidates to join a vibrant and collaborative team of scientists and engineersin 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-phasefluid 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, 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.