Research Scientist (Computational Sustainability Division), IHPC Full-time

Research Scientist (Computational Sustainability Division), IHPC

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 include:

• 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.
• Good written and communication skills.
• Self-motivated, resourceful, and committed to high standards of professional integrity.