CFD & Physics-Informed ML Scientist for Sustainability

A*STAR RESEARCH ENTITIES; Singapore; On-site; SGD 100,000 - 160,000; Full time

Job summary

The Computational Sustainability Division (CoS) at the Institute of Advanced Intelligence and Computing, A*STAR, seeks candidates to advance CFD for urban sustainability, marine-offshore decarbonisation, and low-carbon energy. You will contribute from fundamental capability building to applied research, with strong growth potential and impact.

Responsibilities include developing multi-physics CFD models, Physics-Informed ML, data-driven surrogates, and integrating CFD codes across domains.

Qualifications

• PhD in a relevant engineering/physics field or equivalent research experience.
• Strong understanding of physics/engineering principles incl. fluid dynamics and multi-phase flow.
• Proficiency in numerical methods and high-performance computing.
• Experience with open-source CFD tools (OpenFOAM, Nek5000, Palabos) and optimization.
• Proficiency in programming languages (Python, C/C++, Fortran, CUDA, Julia).

Responsibilities

• Develop CFD modelling capabilities for multi-physics, multi-component flows.
• Develop Physics-Informed ML models and data-driven surrogates.
• Apply CFD codes across environmental and hydrodynamic domains; collaborate with stakeholders.

Skills

Physics/Engineering

Team collaboration

English communication

Self-driven integrity

Education

PhD in Mechanical/Aerospace/Civil/Environmental/Computational Eng/Applied Physics

Tools

OpenFOAM

Nek5000

Palabos

Python

C/C++

Fortran

CUDA

Julia

Job description

The Computational Sustainability Division (CoS) at the Institute of Advanced Intelligence and Computing, A*STAR, seeks candidates to advance CFD for urban sustainability, marine-offshore decarbonisation, and low-carbon energy. You will contribute from fundamental capability building to applied research, with strong growth potential and impact.

Responsibilities include developing multi-physics CFD models, Physics-Informed ML, data-driven surrogates, and integrating CFD codes across domains.