Job Summary
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We are looking for potential candidates to join a vibrant and collaborative team of scientists and engineers in 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-phase fluid 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. |