Scientist, Computational Sustainability

ASTAR is seeking a Scientist in Computational Sustainability to join their Institute of High Performance Computing in Singapore. The role focuses on R&D in computational fluid dynamics (CFD) for urban sustainability and decarbonisation. Responsibilities include developing advanced simulation models, collaborating with multidisciplinary teams, and applying machine learning techniques. Candidates should have a strong academic background in relevant fields, expertise in fluid dynamics, and programming skills.
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), ASTAR. 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 command of written and spoken English; self-motivated, resourceful, and committed to high standards of professional integrity.