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-performance computing, GPU acceleration, automated benchmarking or large-scale numerical workflows. 4. Evidence of self-motivated contributing to collaborative research outputs, open-source software, preprints
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programming skills in Python, MATLAB, C++, or similar scientific computing environments, including high-performance and GPU computing. A strong scientific track record, evidenced by high-quality publications
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transfer (CHT) or fluidic manifold optimization Experience with machine learning/AI (PyTorch or TensorFlow), reduced-order modeling (ROM), or data assimilation (DA) Experience with GPU programming (CUDA and
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will be viewed highly favorably. Experience with GPU acceleration or code parallelization paradigms (e.g., MPI, OpenMP, CUDA). FLSA Exempt Full Time/Part Time Full Time Number of Hours Worked per Week 40
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
computing environments (e.g., GPUs, distributed training). Experience mentoring graduate or undergraduate students. Special Physical/Mental Requirements NA Special Instructions For information on UNC
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managing experiments using GPUs Ability to visualize experimental results and learning curves Effective inter-personal and team-building skills Self-motivated with an ability to work independently and in a
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, TensorFlow) with several years of practice Experience in maintaining high-quality code on Github Experience in running and managing experiments using GPUs Ability to visualize experimental results and learning
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knowledge in parallel/GPU computing. Job Duties Job Duty Doing research problems in the area of mathematical foundations of data science and machine learning. The postdoc will assist with ongoing research
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• Access to modern computational infrastructure, including GPU clusters and supercomputers • Scientific freedom and excellent development opportunities • Salary according to the German public service pay
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. Experience with GPU-based training and high-performance computing. Interest in translating methodological contributions into high-impact medical AI venues (e.g., Nature Medicine, Nature Machine Intelligence