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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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necessary while experience in parallel and GPU computing is most desired.PX4, Pixhawk or equivalent. Ability to work well with team members and good communication skills are essential. To apply
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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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research or prototyping context. Experience with FPGA/RTL development and Xilinx tools, and CUDA coding for GPU acceleration. Knowledge of RF design, signal integrity, and calibration, with experience
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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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. Experience with large-scale C++ projects and version management tools (e.g., GitHub). Experience with STM32 microcontrollers and Xilinx RFSoC platforms, and CUDA coding for GPU acceleration. Apply
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Radio) concepts and lab equipment for experimental research and validation. Experience with protocol implementation and bridging (e.g., JESD204C/eCPRI), and CUDA coding for GPU acceleration. Knowledge
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computing (HPC) environments and GPU programming. Strong teamwork skills with the ability to manage multiple priorities and thrive in a diverse work environment. We regret that only shortlisted
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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