Scientist, Computational Sustainability, IAIC

A*STAR International; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

Institute of High Performance Computing (IHPC) – A*STAR invites outstanding candidates to join the Computational Sustainability Division to contribute to research and development in CFD, addressing urban sustainability, marine decarbonisation, low-carbon energy, and related domains. You will engage in foundational capability development and applied research with ample room for growth.

The role includes developing multi-physics CFD capabilities, implementing Physics-Informed ML models, and

Qualifications

• PhD in a relevant engineering/physical science field preferred.
• Strong foundation in fluid dynamics and transport phenomena.
• Experience with CFD codes and HPC formatting.

Responsibilities

• Develop advanced CFD capabilities for multi-physics, multi-component flows.
• Design Physics-Informed Machine Learning (PIML) models.
• Create physics-based, data-driven surrogates and data assimilation methods.
• Collaborate with multidisciplinary teams to apply CFD to diverse applications.
• Engage with industry partners and affiliated institutes to translate research outcomes.

Skills

Fluid dynamics

CFD

High-performance computing

Python

C/C++

Machine learning

Education

PhD in Mechanical, Aerospace, Civil, Environmental, Chemical, Computational Engineering or related

Tools

OpenFOAM

Nek5000

Palabos

Fortran

CUDA

Job description

Job Title: Scientist, Computational Sustainability, IAIC

Requisition ID: 718

Posting Start Date: 10/04/2026

Job Summary

We invite applications from outstanding candidates to join a dynamic and highly collaborative team of scientists and engineers within the Computational Sustainability Division (CoS) at the Institute of High Performance Computing (IHPC), A*STAR. The successful candidate will contribute to research and development in computational fluid dynamics (CFD), addressing critical challenges in urban sustainability, marine and offshore decarbonisation, low-carbon and renewable energy, and related domains. The role involves working on a broad spectrum of R&D projects, from foundational capability development to applied research, offering significant opportunities for professional growth and meaningful impact.

The key scope of work includes:

• Developing advanced modelling and simulation capabilities for multi-physics, multi-component, and multi-phase fluid flow problems.
• Designing and implementing Physics-Informed Machine Learning (PIML) models, including core methodologies for embedding governing physical principles into machine learning frameworks.
• Developing physics-based, data-driven surrogate models and data assimilation techniques for flow-related problems and applications.
• Working closely with multidisciplinary teams to develop and apply CFD codes across diverse application areas, such as environmental flows, hydrodynamics, turbulent flows, and dispersion modelling.
• Collaborating with industry partners, affiliated research institutes, and other key stakeholders to translate research outcomes into real-world impact.

Job Requirements

• A strong academic background in physics and/or engineering, preferably with a PhD in Mechanical, Aerospace, Civil, Environmental, Chemical, Computational Engineering, Applied Physics, or a closely related discipline.
• Solid understanding of core physics and engineering principles, including fluid dynamics, transport phenomena, and thermodynamics, with demonstrated expertise in multi-phase and multi-component flows.
• In-depth knowledge of numerical methods for fluid flow simulations (e.g. finite volume methods, lattice Boltzmann methods, volume-of-fluid techniques) and experience with high-performance computing.
• Experience in developing computational methods, including the use and customization of open-source CFD codes (e.g. OpenFOAM, Nek5000, Palabos); familiarity with optimization techniques (e.g. linear, nonlinear, and real-time optimization) is an advantage.
• Proficiency in programming languages such as Python, C/C++, Fortran, CUDA, and/or Julia.
• Experience with machine learning techniques, including neural networks and deep learning, is highly desirable.
• Strong interpersonal and communication skills, with the ability to work effectively both independently and as part of a multidisciplinary team; excellent communication; self-motivated, resourceful, and committed to high standards 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.