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engineering; Formal methods, models, and languages; Interactive and cognitive systems; Distributed systems, parallel computing, and networks. The successful candidate will work closely with teams specializing
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. Demonstrated experience developing and running computational tools for high-performance computing environment, including distributed parallelism for GPUs. Demonstrated experience in common scientific programming
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, or deployment at scale. A proven track record of high-quality research contributions published in top-tier machine learning conferences or journals. Proficiency in high-performance computing, distributed and
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/O solutions (e.g., HDF5, ADIOS2), and distributed computing tools relevant to data preparation. Evidence of ability to conduct independent research and publish in peer-reviewed venues. Preferred
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linear algebra computations, building software for scientific applications using GPUs (Graphics Processing Unit), multi-threading and parallelism, numerical discretization methods (finite differences
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
and Experience: Distributed parallel training and parameter-efficient tuning. Familiarity with multi-modal foundation models, HITL techniques, and prompt engineering. Experience with LLM fine-tuning
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distributed systems techniques. Proficiency in programming languages such as Python, C++, or similar, as well as experience with HPC environments and parallel computing. Demonstrated hands-on experience and
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software for multi-arch environments Development in high-performance computing (HPC) or distributed systems Strong understanding of Linux toolchains, build systems (CMake), and debugging tools Parallel
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results. Machine Learning skills to automise comparison process. Unbiased approach to different theoretical models. Experience in HPC system usage and parallel/distributed computing. Knowledge in GPU-based
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hydrodynamics and/or N-body simulations in the star and planet formation context Experience in the field with HPC system usage and parallel/distributed computing Knowledge in GPU-based programming would be