31 computational-physics-superconductor 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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that are relevant to industry demands while working on research projects in SIT. This role supports the Future Ship and System Design (FSSD) Programme, which aims to accelerate the decarbonisation and digitalisation
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training and have opportunities to participate in faculty development programmes. The fellowship is tenable for one year. On an exception basis, a two-year programme may be supported. Service Commitment One
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training and have opportunities to participate in faculty development programmes. The fellowship is tenable for one year. On an exception basis, a two-year programme may be supported. Service Commitment One
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hydrodynamic modelling and sediment transportation. Model Validation: Validation of DHI Mike Model with past historical data Data Analysis: Process and analyze coastal and ocean wave data to improve model
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training and have opportunities to participate in faculty development programmes. The fellowship is tenable for one year. On an exception basis, a two-year programme may be supported. Service Commitment One
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modelling using Delt3D software. The role of the researcher is to perform physics-based modeling to build a numerical model that can predict storm surges in Singapore coastlines based on different weather
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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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to J2 and atmospheric drag. To critically evaluate and validate the design of the controllers, test cases based on the physical and orbital features of existing or planned cubesatellite formation flying
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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