Job Description

Development of multi-phase Smoothed Particle Hydrodynamics (SPH) tools and scientific machine learning approach for wind and wave load predictions at model-scale with preliminary outcomes. Application to practical problems involving Development of multi-phase Smoothed Particle Hydrodynamics (SPH) tools and scientific machine learning approach for wind and wave load predictions at model-scale with preliminary outcomes. Application to practical problems involving Marine & Offshore (M&O) assets (such as moored ships and floating wind turbines) with final deliverable in terms of wind and wave load predictions, as well as fine-tuning of the developed scientific machine learning predictive models. Qualifications . PhD in Mechanical Engineering or relevant Fields. . Candidate must have a strong interest and background in the creation of computational data from High Fidelity Computational Continuum Mechanics (CCM) Simulations i.e. CFD, CSD, 6-DOF Dynamics using open source solvers for multi-phase fluid dynamics of the air-sea interface to provide estimates of unsteady aerodynamic and wave loading on offshore superstructures, creation of reduced order models from these high fidelity data to train physics informed deep learning networks for fast and reliable hydrodynamic and wave load predictions on marine offshore superstructures. . While the candidate is expected to have experience working with open source CCM tools such as OpenFOAM, Fluidity, SPH and FENICS, and since this is a project in progress, the interest in developing tools using DualSPHysics will be the prime requirement/interest for this position. Additionally, the candidate is expected to integrate wind turbine models in the context of SPH. The candidate must also be familiar with Linux OS and parallel computing technologies and be adept at using High Performance Computing Platforms such as multi-core and GPU systems for all high fidelity computational simulations. . Candidate should also be interested in model order reduction techniques and able to use these data to construct reliable machine learning surrogates for prediction using Tensorflow / deep learning / python tools and be able to demonstrate this capability and is expected to build on the existing efforts in progress in this area for which training data is generated using DualSPHysics. Covid-19 Message At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presence is required. Taking into consideration the health and well-being of our staff and students and to better protect everyone in the campus, applicants are strongly encouraged to have themselves fully COVID-19 vaccinated to secure successful employment with NUS. Department : Mechanical Engineering Employee Referral Eligible: No Job requisition ID : 17867