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turbulent flow simulations. 3. Background in numerical methods and scientific computing. 4. Proficiency in Python programming and high-performance computing on modern GPU-based platforms
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computational modelling of additive manufacturing and develop high-performance GPU-based CFD solvers. Qualifications • With PhD degree • Strong research experience in developing GPU-based
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. Understanding of local hardware optimization (GPU/VRAM management). Comfortable putting together a simple working interface for a workflow (e.g. Gradio) — no formal UX/UI design background required. Able
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hardware (e.g., GPUs and/or Non-volatile memory) and data science applications.
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computing environments and GPU computing. Proven experience in weather and climate models development and applications. Experience in machine learning, deep learning, or AI applications for atmospheric
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. Experience with embedded systems and hardware/software integration in a research or prototyping context. Experience with FPGA/RTL development and Xilinx tools, and CUDA coding for GPU acceleration. Knowledge
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systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU acceleration is a plus. Strong leadership, communication, and documentation skills.
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bridging (e.g., JESD204C/eCPRI), and CUDA coding for GPU acceleration. Knowledge of RF calibration, test automation, and signal integrity in a research or prototyping context. Track record of peer-reviewed
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experiences on computer vision • Strong programming skills in using Deep Learning tools like PyTorch and GPU clusters. • Good written and verbal communications. • Open to Fixed Term Contract
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RFSoC platforms, and CUDA coding for GPU acceleration.