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well as large-scale GPU computing facilities for deep learning. We are looking for a Research Engineer to manage the EEE GPU Cluster. The role will focus on enhancing the EEE GPU Cluster team’s ability in terms
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development skills Model deployment (e.g., ONNX, TensorRT) Edge computing or embedded vision systems (e.g., NVIDIA Jetson Nano) Real-time processing and GPU acceleration Experience working on industry R&D
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well as large-scale GPU computing facilities for deep learning. Our Lab aims to hire a Research Fellow to lead a research project on Real-World Deepfake Detection and Image Forgery Localization. The role will
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strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high
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strategies for large-scale or streaming data. Develop parallelized and GPU-accelerated learning modules, ensuring scalability and performance efficiency. Build and maintain robust data pipelines for high
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. Conduct experimental studies using GPU-enabled computing resources for model training, inference, and simulation-based evaluation. Support rapid prototyping and iteration of research ideas, from concept
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model. Further your knowledge in quantitative modeling and financial analysis. Involve yourself in emerging technologies (e.g. cloud/grid computing, GPU computing, FPGA) in the Fintech field. Benefit from
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-mode taxonomies). Implement and maintain high-quality research codebases (PyTorch/HF), experiment tracking, and compute workflows (multi-GPU, HPC/cluster), ensuring reproducibility and documentation
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to diverse academic and industrial audiences. Proficiency in Python and deep learning frameworks such as PyTorch. Experience with Linux environments and GPU cluster management is essential. Competent in
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research programme Access to secure clinical and multi-omics data environments Modern GPU, and high-performance computing resources, plus dedicated research-engineering support Close integration with