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-to-Grid Integration through Intelligent Optimization and Predictive Control at SIT’s Punggol campus. Under this job title, you will be required to establish a test bed in SIT’s Punggol campus building
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collaboration with IHL and an industry partner. where you will be part of the research team to develop and demonstrate Building-to-Grid Integration through Intelligent Optimization and Predictive Control at SIT’s
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in the development, validation, and optimization of 3D-printed Ship Hull and Connector System. This includes conducting finite element modeling, ship resistance and stability assessments, as
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and optimize high-frequency magnetic components and resonant power conversion circuits for advanced aerospace power supply applications. In this role, you will collaborate closely with project
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of model deployment and optimization (e.g., ONNX, TensorRT, model quantization) Experience with edge computing or embedded systems (e.g., NVIDIA Jetson, Raspberry Pi) Background in real-time processing and
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, and optimize algorithms for contact-based mobile manipulation. Apply reinforcement learning, imitation learning, and motion / manipulation skill learning to real robot tasks. Integrate learned policies
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developing optimized modules in C#/C++ within Unity and/or Unreal Engine Experience with database management systems For network engineer role: Experience in computer networking, multiplayer architecture, and
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physiology and adaptation for industrial applications; high-throughput screening and automation for strain optimization; bioprospecting for novel microbial hosts, enzymes, and metabolites to support
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that are relevant to industry demands while working on research projects in SIT. We are seeking a talented Power Electronics Engineer or Research Fellow to design and optimize high-frequency magnetic components and
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, planning, and control within a real-world robotic system. Job Responsibilities： Develop, implement, and optimize algorithms for contact-based mobile manipulation. Apply reinforcement learning, imitation