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, Probabilistic Inference, Algebraic Topology and Wavelet analysis theory. Familiar with Matlab/Python/C++ programming. Experience with Pytorch and multi-GPU model deployment. Experience in analyzing complex
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needs, such as assisting the team with evaluating evolutionary algorithms for exploring creative new hand designs, or reinforcement learning for policy optimisation, all within a huge GPU-based simulation
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https://scholar.google.com/citations?user=9IRAYdEAAAAJ& ;hl=en and https://www.physics.sjtu.edu.cn/amgg/ Research profile: Candidates with a previous background on GPU computing are especially encouraged
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in GPU programming one or more parallel computing models, including SYCL, CUDA, HIP, or OpenMP Experience with scientific computing and software development on HPC systems Ability to conduct
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turbulence. Experience with GPU programming, FPGA, and DNN in image recognition is a great plus. Track record of publications and conference presentations. Experience with hands on lab work. FLSA Exempt Full
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. First, efficient and scalable training procedure are still needed, irrespective of whether the training is done off-line on a traditional GPU-based architecture, on neuromorphic hardware. Second
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computational infrastructure such as A100 and H100 GPUs, combined with pre-processed large-scale biobank data such as UK Biobank and ADSP, enabling you to work at the scale required for breakthrough research
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, with PyTorch and/or other GPU programming tools is also necessary. You should have completed all requirements for your PhD by the time you are hired. How to Apply: Candidates who have most, but not all
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, Matlab Preferred Qualifications: Experience in thermos-fluids in porous media. Experience in High-Performance Computing (HPC) on CPU or GPU platforms. Experience in mentoring of graduate and undergraduate
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), or reinforcement learning (RL) post-training. Experience with multi-GPU training and a strong working knowledge of reinforcement learning are also required. Familiarity with standard software development tools