29 postdoc-computational-fluid-dynamics-"Prof" Fellowship positions at Singapore Institute of Technology
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that are relevant to industry demands while working on research projects in SIT. The primary responsibility of this role is to deliver on a Pharmaceutical Innovation Programme Singapore research project where you
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the project: 1. Computational Fluid Dynamics (CFD) Simulations: • Develop and implement CFD models to simulate the effects of mangroves, seagrass, and coral on wave attenuation. • Conduct parametric studies
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behavior of floating breakwaters under varied sea conditions. 2. CFD and Finite Element Modeling • Perform Computational Fluid Dynamics (CFD) simulations to optimize the design and performance of floating
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team to lead the wave and hydrodynamic modelling to study the impact of mangroves and seagrass on coastal dynamics and sediment transportation. As a specialist in wave modelling software, the successful
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: Delft3D and SWAN Modelling: Utilize Delft3D and SWAN software to develop and validate numerical models for wave propagation, storm surge modelling, and coastal dynamics. Building up of a database of sea
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will force the dynamical performance of one or several followers, with respect to a leader, to asymptotically track a time-varying nominal trajectory, while the threat of collisions between formation
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cases to demonstrate performance and recommend improvements at unit, sub-system and whole system level. Dynamic, thermal, and stability analysis of the electrical propulsion systems of various e-vessel
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Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: i. Carry out dynamic modelling and simulation of the energy storage
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the industry to lead and/or conduct innovative research on, but not limited to evolutionary computing, job scheduling, transfer optimization, transfer learning, reinforcement learning, large-scale