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in computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in
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networks. Experience securing research funding, managing research programs, and building academic partnerships. Experience with embedded systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU
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background in computer systems, GPU programming. Experience with CUDA, Triton, PyTorch, and LLM inference frameworks. Experience in research and good publication record. Track record first-author publications
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of algorithms and high-performance (GPU) computing; Industrial and Systems Engineering, with AI in process mapping and Conops design. With SUTD located next to the number #1-ranked airport in the world
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, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and
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and GPU acceleration Experience working on industry R&D projects Key Competencies Able to build and maintain strong working relationships with team members, stakeholders, and external partners Self
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, scalability, and cost requirements. • HPC Management: Design, develop, and optimize resource management tools and Slurm GPU plugins for supercomputing clusters. • Deployment & Automation: Oversee
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in hardware–software integration, GPU/FPGA acceleration, or translation of research into prototype systems would be an added advantage. Ability to work independently while contributing effectively in